VISAPP 2007 Abstracts

Area 1 - Image Formation and Processing
Full Papers Short Papers
Area 2 - Image Analysis
Full Papers Short Papers
Area 3 - Image Understanding
Full Papers Short Papers Rejecteds
Area 4 - Motion, Tracking and Stereo Vision
Full Papers Short Papers Rejecteds
Full Papers
Paper Nr: 377
Title:

INTEGRATING IMAGING AND VISION FOR CONTENT-SPECIFIC IMAGE ENHANCEMENT
Authors:

Gianluigi Ciocca, Claudio Cusano, Francesca Gasparini and Raimondo Schettini
Abstract: The quality of real-world photographs can often be considerably improved by digital image processing .In this article we describe our approach, integrating imaging and vision, for content-specific image enhancement. According to our approach, the overall quality of digital photographs is improved by a modular, image enhancement procedure driven by the image content. Single processing modules can be considered as autonomous elements. The modules can be combined to improve the overall quality according to image and defect categories.
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Paper Nr: 415
Title:

IMAGE ENHANCEMENT BY REGION DETECTION ON CFA DATA IMAGES
Authors:

Sebastiano Battiato, S. Cariolo, Giovanni Gallo and G. Di Blasi
Abstract: The paper proposes a new method devoted to identify specific semantic regions on CFA (Color Filtering Array) data images representing natural scenes. Making use of collected statistics over a large dataset of high quality natural images, the method uses spatial features and the Principal Component Analysis (PCA) in the HSL and normalized-RG color spaces. The classes considered, taking into account “visual significance”, are skin, vegetation, blue sky and sea. Semantic information are obtained on pixel basis leading to meaningful regions although not spatially coherent. Such information is used for automatic color rendition of natural digital images based on adaptive color correction. The overall method outperforms previous results providing reliable information validated by measured and subjective experiments.
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Paper Nr: 430
Title:

TOWARDS INTENT DEPENDENT IMAGE ENHANCEMENT - State-of-the-art and Recent Attempts
Authors:

Marco Bressan, Gabriela Csurka and Sebastien Favre
Abstract: Image enhancement is mostly driven by intent and its future largely relies on our ability to map the space of intentions with the space of possible enhancements. Taking into account the semantic content of an image is an important step in this direction where contextual and aesthetic dimensions are also likely to have an important role. In this article we detail the state-of-the-art and some recent efforts in for semantic or content-dependent enhancement. Through a concrete example we also show how image understanding and image enhancement tools can be brought together. We show how the mapping between semantic space and enhancements can be learnt from user evaluations when the purpose is subjective quality measured by user preference. This is done by introducing a discretization of both spaces and notions of coherence, agreement and relevance to the user response. Another example illustrates the feasibility of solving the situation where the binary option of whether or not to enhance is considered.
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Area 1 - Image Formation and Processing

Full Papers
Paper Nr: 69
Title:

HUE VARIANCE PREDICTION - An Empirical Estimate of the Variance within the Hue of an Image
Authors:

Robert N. Grant, Richard Green and Adrian Clark
Abstract: In the area of vision-based local environment mapping, inconsistent lighting can interfere with a robust system. The HLS colour model can be useful when working with varying illumination as it tries to separate illumination levels from hue. This means that using hue information can result in an image invariant to illumination. This can be valuable when trying to determine object boundaries, object identification and image correspondence. The problem is that noise is greater at lower illumination levels. While removing the illumination effects on the image, separating out hue means that the noise effects of non-optimal illumination remain. This paper looks at how the known illumination information of pixels can be used to accurately predict and reduce noise in the hue obtained in video from a colour digital camera.
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Paper Nr: 79
Title:

EVALUATING STITCHING QUALITY
Authors:

Jani Boutellier, Olli Silvén, Lassi Korhonen and Marius Tico
Abstract: Until now, there has been no objective measure for the quality of mosaic images or mosaicking algorithms. To mend this shortcoming, a new method is proposed. In this new approach, the algorithm that is to be tested, acquires a set of synthetically created test images for constructing a mosaic. The synthetic images are created from a reference image that is also used as a basis in the evaluation of the image mosaic. To simulate the effects of actual photography, various camera-related distortions along with perspective warps, are applied to the computer-generated synthetic images. The proposed approach can be used to test all kinds of computer-based stitching algorithms and presents the computed mosaic quality as a single number.
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Paper Nr: 95
Title:

AUTOMATED SEPARATION OF REFLECTIONS FROM A SINGLE IMAGE BASED ON EDGE CLASSIFICATION
Authors:

Kenji Hara, Kohei Inoue and Kiichi Urahama
Abstract: Looking through a window, the object behind the window is often disturbed by a reflection of another object. In the paper, we present a new method for separating reflections from a single image. Most existing techniques require the programmer to create an image database or require the user to manually provide the position and layer information of feature points in the input image, and thus suffer from being extremely laborious. Our method is realized by classifying edges in the input image based on the belonging layer and formalizing the problem of decomposing the single image into two layer images as an optimization problem easier to solve based on this classification, and then solving this optimization with a pyramid structure and deterministic annealing. As a result, we are able to accomplish almost fully automated separation of reflections from a single image.
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Paper Nr: 102
Title:

A CLOSED-FORM SOLUTION FOR THE GENERIC SELF-CALIBRATION OF CENTRAL CAMERAS FROM TWO ROTATIONAL FLOWS
Authors:

Ferran Espuny
Abstract: In this paper we address the problem of self-calibrating a differentiable generic camera from two rotational flows defined on an open set of the image. Such a camera model can be used for any central smooth imaging system, and thus any given method for the generic model can be applied to many different vision systems. We give a theoretical closed-form solution to the problem, proving that the ambiguity in the obtained solution is metric (up to an orthogonal linear transformation). Based in the theoretical results, we contribute with an algorithm to achieve metric self-calibration of any central generic camera using two optical flows observed in (part of) the image, which correspond to two infinitesimal rotations of the camera.
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Paper Nr: 123
Title:

IMPROVEMENT OF DEBLOCKING CORRECTIONS ON FLAT AREAS AND PROPOSAL FOR A LOW-COST IMPLEMENTATION
Authors:

Frederique Crete, Marina Nicolas and Patricia Ladret
Abstract: The quantization of data from individual block-based Discrete Cosine Transform generates the blocking effect estimated as the most annoying compression artefact. It appears as an artificial structure caused by noticeable changes in pixel values along the block boundaries. Due to the masking effect, the blocking artefact is more annoying in flat areas than in textured or detailed areas. Existing low-cost algorithms propose strong low-pass filters to correct this artefact in flat areas. Nevertheless, they are confronted to a limitation based on their filter length. This limitation can introduce other artefacts such as ghost boundaries. We propose a new principle to detect and correct the boundaries on flat areas without being limited to a fix number of pixels. This principle can be easily implemented in a low-cost post processing algorithm and completed with other corrections for perceptible boundaries on non-flat areas. This new method produces results which are perceived as more pleasing for the human eye than the other traditional low-cost methods.
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Paper Nr: 167
Title:

THRESHOLD DECOMPOSITION DRIVEN ADAPTIVE MORPHOLOGICAL FILTER FOR IMAGE SHARPENING
Authors:

Tarek Mahmoud and Stephen Marshall
Abstract: A new method is proposed to sharpen digital images. This sharpening method is based on edge detection and a class of morphological filtering. Motivated by the success of threshold decomposition, gradient-based operators, such as Prewitt operators, are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the performance of these detected edge deblurring filters is superior to that of the traditional sharpening filter family.
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Paper Nr: 188
Title:

GAP FILLING IN 3D VESSEL LIKE PATTERNS WITH TENSOR FIELDS - Application to High Resolution Computed Tomography Images of Vessel Networks
Authors:

Laurent Risser, Franck Plouraboué and Xavier Descombes
Abstract: We present an algorithm for merging discontinuities in three-dimensional (3D) images of tubular structures. The application of the proposed method is associated with large 3D images presenting undesirable discontinuities. In order to recover the real network topology, we need to fill the gap between the closest discontinuous tubular segments. We present a new algorithm to achieve this goal based on a tensor voting method. This algorithm is robust, relatively fast and does not require numerous parameters nor manual intervention. Representative results are illustrated on real 3D micro-vascular networks.
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Paper Nr: 236
Title:

ADAPTIVE DATA-DRIVEN REGULARIZATION FOR VARIATIONAL IMAGE RESTORATION IN THE BV SPACE
Authors:

Hongwei Zheng and Olaf Hellwich
Abstract: We present a novel variational regularization in the space of functions of Bounded Variation (BV) for adaptive data-driven image restoration. The discontinuities are important features in image processing. The BV space is well adapted for the measure of gradient and discontinuities. More over, the degradation of images includes not only random noises but also multiplicative, spatial degradations, i.e., blur. To achieve simultaneous image deblurring and denoising, a variable exponent linear growth functional on the BV space is extended in Bayesian estimation with respect to deblurring and denoising. The selection of regularization parameters is self-adjusting based on spatially local variances. Simultaneously, the linear and non-linear smoothing operators are continuously changed following the strength of discontinuities. The time of stopping the process is optimally determined by measuring the signal-to-noise ratio. The algorithm is robust in that it can handle images that are formed with different types of noises and blur. Numerical experiments show that the algorithm achieves more encouraging perceptual image restoration results.
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Paper Nr: 314
Title:

ROBUST CAMERA CALIBRATION - A Generic, Optimization-based Approach
Authors:

Stephan Rupp and Matthias Elter
Abstract: The estimation of camera parameters is a fundamental step for many image guided applications in the industrial and medical field, especially when the extraction of 3d information from 2d intensity images is in the focus of a particular application. Usually, the estimation process is called camera calibration and it is performed by taking images of a special calibration object. From these shots the image coordinates of the projected calibration marks are extracted and the mapping from the 3d world coordinates to the 2d image coordinates is calculated. To attain a well-suited mapping, the calibration images must suffice certain constraints in order to ensure that the underlying mathmatical algorithms are well-posed. Thus, the quality of the estimation severly depends on the choice of the input images. In this paper we propose a generic calibration framework that is robust against ill-posed images as it determines the subset of images yielding the optimal model fit error with respect to a certain quality measure.
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Paper Nr: 333
Title:

CIRCULAR PROCESSING OF THE HUE VARIABLE - A Particular Trait of Colour Image Processing
Authors:

Alfredo Restrepo, Carlos Rodríguez and Camilo Vejarano
Abstract: Novel tools for colour image processing are presented. Unlike many magnitudes dealt with in engineering, the hue variable of a colour image is circular and requires a special treatment. Special techniques have been advanced in statistics for the analysis of data from angular variables; likewise in image processing for the processing of the hue variable. We give a definition of the median and of the range of angular data and apply their running versions on images to smooth them and to detect hue edges. We also give definitions of hue morphology; one based on the topological concept of lifting and on grey level morphology; another definition is wholly given in a circular context.
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Short Papers
Paper Nr: 6
Title:

VECTOR QUANTISATION BASED IMAGE ENHANCEMENT
Authors:

W. P. Cockshott, Sumitha L. Balasuriya, Irwan Prasetya Gunawan and J. Paul Siebert
Abstract: We present a new algorithm for rescaling images inspired by fractal coding. It uses a statistical model of the relationship between detail at different scales of the image to interpolate detail at one octave above the highest spatial frequency in the original image. We compare it with Bspline and bilinear interpolation techniques and show that it yields a sharper looking rescaled image.
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Paper Nr: 75
Title:

SKEW CORRECTION IN DOCUMENTS WITH SEVERAL DIFFERENTLY SKEWED TEXT AREAS
Authors:

P. Saragiotis and Nikos Papamarkos
Abstract: In this paper we propose a technique for detecting and correcting the skew of text areas in a document. The documents we work with may contain several areas of text with different skew angles. In the first stage, a text localization procedure is applied based on connected components analysis. Specifically, the connected components of the document are extracted and filtered according to their size and geometric characteristics. Next, the candidate characters are grouped using a nearest neighbour approach to form words, in a first step, and then text lines of any skew, in a second step. Using linear regression, two lines are estimated for each text line representing its top and bottom boundaries. The text lines in near locations with similar skew angles are grown to form text areas. These text areas are rotated independently to a horizontal or vertical plane. This technique has been tested and proved efficient and robust on a wide variety of documents including spreadsheets, book and magazine covers and advertisements.
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Paper Nr: 159
Title:

A ROBUST WATERMARKING SCHEME BASED ON EDGE DETECTION AND CONTRAST SENSITIVITY FUNCTION
Authors:

John Ellinas and Dimitrios Manolakis
Abstract: The efficiency of an image watermarking technique depends on the preservation of visually significant information. This is attained by embedding the watermark transparently with the maximum possible strength. The current paper presents an approach for still image digital watermarking in which the watermark embedding process employs the wavelet transform and incorporates Human Visual System (HVS) characteristics. The sensitivity of a human observer to contrast with respect to spatial frequency is described by the Contrast Sensitivity Function (CSF).The strength of the watermark within the decomposition subbands, which occupy an interval on the spatial frequencies, is adjusted according to this sensitivity. Moreover, the watermark embedding process is carried over the subband coefficients that lie on edges where distortions are less noticeable. The experimental evaluation of the proposed method shows very good results in terms of robustness and transparency.
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Paper Nr: 213
Title:

SPHERICAL IMAGE DENOISING AND ITS APPLICATION TO OMNIDIRECTIONAL IMAGING
Authors:

Stephanie Bigot, Kachi Djemaa, Sylvain Durand and El Mustapha Mouaddib
Abstract: This paper addresses the problem of spherical image processing. Thanks to projective geometry, the omnidirectional image can be presented as a function on sphere S2 . The target application includes omnidirectional image smoothing. We describe a new method of smoothing for spherical images. For that purpose, we introduce a suitable Wiener filter and we use the Tikhonov method to these images. In order to compare their performances, we present the most used classical spherical kernels. We present several examples for filtering real and synthetical spherical images.
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Paper Nr: 288
Title:

A FAST AND EFFICIENT METHOD FOR CHECK IMAGE QUALITY ASSESSMENT
Authors:

Raju Gupta, Tavneet Batra, Pramod Kumar and Dinesh Ganotra
Abstract: With the enactment of check 21 Act, check image quality has become a critical requirement. Banks responsible for capturing check images (check truncation) have to warrant their images. With a plethora of capturing devices and outsourcing of check acquisition process, assurance of check quality becomes complex. Currently, banks deploy separate subsystem for Image Quality Analysis (IQA), which is based on defect metrics defined by Financial Services Technology Consortium (FSTC) Phase I project (Image quality and usability assurance, 2004). The problem with this approach is that IQA cannot match the scanning speed and has to be deployed as a separate process. Another problem with a predefined defect metrics is that it is dependent on check content. This paper proposes a fast and efficient method to estimate the quality and usability of check images. The method is independent of the check content or layout. IQA based on this algorithm can be deployed at the scanning stage. The checks will have a pre-printed pattern in the form of a logo. This pattern will be detected and analysed for quality and usability. The results show that our algorithm is able to sort unusable check images efficiently. In future, we plan to use this pre-printed pattern as a measure of check security.
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Paper Nr: 290
Title:

A SELF-CALIBRATING CHROMINANCE MODEL APPLIED TO SKIN COLOR DETECTION
Authors:

Jeroen Lichtenauer, Marcel Reinders and Emile Hendriks
Abstract: In case of the absence of a calibration procedure, or when there exists a color difference between direct and ambient light, standard chrominance models are not completely brightness invariant. Therefore, they cannot provide the best space for robust color modeling. Instead of using a fixed chrominance model, our method estimates the actual dependency between color appearance and brightness. This is done by fitting a linear function to a small set of color samples. In the resulting self-calibrated chromatic space, orthogonal to this line, the color distribution is modeled as a 2D Gaussian distribution. The method is applied to skin detection, where the face provides the initialization samples to detect the skin of hands and arms. A comparison with fixed chrominance models shows an overall improvement and also an increased reliability of detection performance in different environments.
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Paper Nr: 315
Title:

A ROBUST IMAGE WATERMARKING TECHNIQUE BASED ON SPECTRUM ANALYSIS AND PSEUDORANDOM SEQUENCES
Authors:

Anastasios L. Kesidis and Basilios Gatos
Abstract: In this paper a watermarking scheme is presented that embeds the watermark message in randomly chosen coefficients along a ring in the frequency domain using non maximal pseudorandom sequences. The proposed method determines the longest possible sequence that corresponds to each watermark bit for a given number of available coefficients. Furthermore, an extra parameter is introduced that controls the robustness versus security performance of the encoding process. This parameter defines the size of a subset of available coefficients in the transform domain which are used for watermark embedding. Experimental results show that the method is robust to a variety of image processing operations and geometric transformations.
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Paper Nr: 316
Title:

MAGE BASED STEGANOGRAPHY AND CRYPTOGRAPHY
Authors:

Domenico Bloisi and Luca Iocchi
Abstract: In this paper we describe a method for integrating together cryptography and steganography through image processing. In particular, we present a system able to perform steganography and cryptography at the same time using images as cover objects for steganography and as keys for cryptography. We will show such system is an effective steganographic one (making a comparison with the well known F5 algorithm) and is also a theoretically unbreakable cryptographic one (demonstrating its equivalence to the Vernam Cipher).
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Paper Nr: 321
Title:

METHODS USED IN INCREASED RESOLUTION PROCESSING - Polygon Based Interpolation and Robust Log-polar Based Registration
Authors:

Stefan Van Der Walt and Ben Herbst
Abstract: A polygon-based interpolation algorithm is presented for use in stacking RAW CCD images. The algorithm improves on linear interpolation in this scenario by closely describing the underlying geometry. 25 frames are stacked in a comparison. When stacking images, it is required that these images are accurately aligned. We present a novel implementation of the log-polar transform that overcomes its prohibitively expensive computation, resulting in fast, robust image registration. This is demonstrated by registering and stacking CCD frames of stars taken by a telescope.
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Paper Nr: 355
Title:

DEMOSAICING LOW RESOLUTION QVGA BAYER PATTERN
Authors:

Tommaso Guseo and Emanuele Menegatti
Abstract: In this paper, we present a solution for the interpolation of low resolution digital images. Many digital cameras can function in two resolution modes: VGA (i.e., 640×480) and QVGA (i.e., 320×240). These cameras use a single sensor covered with a Color Filter Array (CFA). The CFA allows only one color component to be measured at each pixel, the remaining color components must be interpolated, this operation is called demosaicing. There is not a standard way to interpolate the QVGA Bayer pattern and most of the known demosaicing algorithms are not suitable. In this paper, we propose a new solution for the interpolation of QVGA Bayer pattern. Experimental results using digital images and an evaluation function confirm the effectiveness of the interpolation method. The use of the QVGA resolution is important in low-cost and low-power embedded hardware. As an application, we chose the RoboCup domain and in particular our Robovie-M humanoid robot competing in the RoboCup Kid-Size Humanoids League.
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Paper Nr: 62
Title:

RAPID DEVELOPMENT OF RETINEX ALGORITHM ON TI C6000-BASED DIGITAL SIGNAL PROCESSOR
Authors:

Juan Zapata and Ramón Ruiz
Abstract: The Retinex is an image enhancement algorithm that improves the brightness, contrast and sharpness of an image. This work discusses an easy and rapid DSP implementation of the Retinex algorithm on a hardware/software platform which integrates MATLAB/Simulink, Texas Instruments (TI) eXpressDSP Tools and C6000 digital signal processing (DSP) target. This platform automates rapid prototyping on C6000 hardware targets because lets use Simulink to model the Retinex algorithm from blocks in the Signal Processing Blockset, and then use Real-Time Workshop to generate C code targeted to the TI DSP board by mean Code Composer Studio (CCS IDE). The build process downloads the targeted machine code to the selected hardware and runs the executable on the digital signal processor. After downloading the code to the board, our Retinex application runs automatically on our target. It performs a non-linear spatial/spectral transform that synthesizes strong local contrast enhancement. The library real time data exchange (RTDX) instrumentation that contains RTDX input and output blocks let transfer image to and from memory on any C6000-based target.
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Paper Nr: 70
Title:

IMAGE AND VIDEO NOISE - A Comparison of Noise in Images and Video With Regards to Detection and Removal
Authors:

Adrian Clark, Richard Green and Robert Grant
Abstract: Despite the steady advancement of digital camera technology, noise is an ever present problem with image processing. Low light levels, fast camera motion, and even sources of electromagnetic fields such as electric motors can degrade image quality and increase noise levels. Many approaches to remove this noise from images concentrate on a single image, although more data relevant to noise removal can be obtained from video streams. This paper discusses the advantages of using multiple images over an individual image when removing both local noise, such as salt and pepper noise, and global noise, such as motion blur.
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Paper Nr: 126
Title:

COLOR CALIBRATION OF AN ACQUISITION DEVICE - Method, Quality and Results
Authors:

V. Vurpillot, Legrand Anne-claire and A. Tremeau
Abstract: Color calibrated acquisition is of strategic importance when high quality imaging is required, such as for work of art imaging. The aim of calibration is to correct raw acquired image for the various acquisition device signal deformation, such as noise, lighting uniformity, white balance and color deformation, due, for a great part, to camera spectral sensitivities. We first present reference color data computation obtained from camera’s spectral sensitivities and reflectance of reference patches, taken form Gretag MacBeth Color Chart DC. Then we give a color calibration method based on linear regression. We finally evaluate the quality of applied calibration and present some resulting calibrated images.
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Paper Nr: 168
Title:

ADAPTIVE IMAGE RESTORATION USING A LOCAL NEURAL APPROACH
Authors:

Ignazio Gallo, Elisabetta Binaghi and A. Macchi
Abstract: This work aims at defining and experimentally evaluating an iterative strategy based on neural learning for blind image restoration in the presence of blur and noise. A salient aspect of our solution is the local estimation of the restored image based on gradient descent strategies able to estimate both the blurring function and the regularized terms adaptively. Instead of explicitly defining the values of local regularization parameters through predefined functions, an adaptive learning approach is proposed. The method was evaluated experimentally using a test pattern generated by a function checkerboard in Matlab. To investigate whether the strategy can be considered an alternative to conventional restoration procedures the results were compared with those obtained by a well known neural restoration approach.
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Paper Nr: 271
Title:

AN UNSUPERVISED SONAR IMAGES SEGMENTATION APPROACH
Authors:

Abdel-Ouahab Boudraa and Jean-Christophe Cexus
Abstract: In this work an unsupervised Sonar (Sound navigation and ranging) images segmentation is proposed. Due to the textural nature of the Sonar images, a band-pass filtering that takes into account the local spatial frequency of these images is proposed. Sonar image is passed through a bank of Gabor filters and the filtered images that possess a significant component of the original image are selected. To calculate the radial frequencies, a new approach is proposed. The selected filtered images are then subjected to a non-linear transformation. An energy measure is defined on the transformed images in order to compute texture features. The texture energy features are used as input to a clustering algorithm. The segmentation scheme has been successfully tested on real high-resolution Sonar images, yielding very promising results.
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Area 2 - Image Analysis

Full Papers
Paper Nr: 64
Title:

SIMULTANEOUS ROBUST FITTING OF MULTIPLE CURVES
Authors:

Jean-philippe Tarel, Pierre Charbonnier and Sio-song Ieng
Abstract: In this paper, we address the problem of robustly recovering several instances of a curve model from a single noisy data set with outliers. Using M-estimators revisited in a Lagrangian formalism, we derive an algorithm that we call SMRF, which extends the classical Iterative Reweighted Least Squares algorithm (IRLS). Compared to the IRLS, it features an extra probability ratio, which is classical in clustering algorithms, in the expression of the weights. Potential numerical issues are tackled by banning zero probabilities in the computation of the weights and by introducing a Gaussian prior on curves coefficients. Applications to camera calibration and lane-markings tracking show the effectiveness of the SMRF algorithm, which outperforms classical Gaussian mixture model algorithms in the presence of outliers.
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Paper Nr: 65
Title:

MODELING NON-GAUSSIAN NOISE FOR ROBUST IMAGE ANALYSIS
Authors:

Sio-song Ieng, Jean-philippe Tarel and Pierre Charbonnier
Abstract: Accurate noise models are important to perform reliable robust image analysis. Indeed, many vision problems can be seen as parameter estimation problems. In this paper, two noise models are presented and we show that these models are convenient to approximate observation noise in different contexts related to image analysis. In spite of the numerous results on M-estimators, their robustness is not always clearly addressed in the image analysis field. Based on Mizera and Mu¨ ller’s recent fundamental work, we study the robustness of M-estimators for the two presented noise models, in the fixed design setting. To illustrate the interest of these noise models, we present two image vision applications that can be solved within this framework: curves fitting and edge-preserving image smoothing.
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Paper Nr: 118
Title:

VIDEO MODELING USING 3-D HIDDEN MARKOV MODEL
Authors:

Joakim Jitén and Bernard Merialdo
Abstract: Statistical modeling methods have become critical for many image processing problems, such as segmentation, compression and classification. In this paper we are proposing and experimenting a computationally efficient simplification of 3-Dimensional Hidden Markov Models. Our proposed model relaxes the dependencies between neighboring state nodes to a random uni-directional dependency by introducing a three dimensional dependency tree (3D-DT HMM). To demonstrate the potential of the model we apply it to the problem of tracking objects in a video sequence. We explore various issues about the effect of the random tree and smoothing techniques. Experiments demonstrate the potential of the model as a tool for tracking video objects with an efficient computational cost.
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Paper Nr: 139
Title:

PARAMETRIZATION, ALIGNMENT AND SHAPE OF SPHERICAL SURFACES
Authors:

Xiuwen Liu, John Bowers and Washington Mio
Abstract: We develop parametrization and alignment techniques for shapes of spherical surfaces in 3D space with the goals of quantifying shape similarities and dissimilarities and modeling shape variations observed within a class of objects. The parametrization techniques are refinements of methods due to Praun and Hoppe and yield parametric mesh representations of spherical surfaces. The main new element is an automated technique to align parametric meshes for shape interpolation and comparison. We sample aligned surfaces at the vertices of a dense common mesh structure to obtain a representation of the shapes as organized point-clouds. We apply Kendall’s shape theory to these dense point clouds to define geodesic shape distance, to obtain geodesic interpolations, and to study statistical properties of shapes that are relevant to problems in computer vision. Applications to the construction of compatible texture maps for a family of surfaces are also discussed.
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Paper Nr: 177
Title:

ROBUST APPEARANCE MATCHING WITH FILTERED COMPONENT ANALYSIS
Authors:

Fernando De La Torre, Alvaro Collet, Jeff Cohn and Takeo Kanade
Abstract: Appearance Models (AM) are commonly used to model appearance and shape variation of objects in images. In particular, they have proven useful to detection, tracking, and synthesis of people’s faces from video. While AM have numerous advantages relative to alternative approaches, they have at least two important drawbacks. First, they are especially prone to local minima in fitting; this problem becomes increasingly problematic as the number of parameters to estimate grows. Second, often few if any of the local minima correspond to the correct location of the model error. To address these problems, we propose Filtered Component Analysis (FCA), an extension of traditional Principal Component Analysis (PCA). FCA learns an optimal set of filters with which to build a multi-band representation of the object. FCA representations were found to be more robust than either grayscale or Gabor filters to problems of local minima. The effectiveness and robustness of the proposed algorithm is demonstrated in both synthetic and real data.
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Paper Nr: 193
Title:

COMPACT (AND ACCURATE) EARLY VISION PROCESSING IN THE HARMONIC SPACE
Authors:

Silvio P. Sabatini, Giulia Gastaldi, Fabio Solari, Karl Pauwels, Marc van Hulle, Javier Diaz, Eduardo Ros, Nicolas Pugeault and Norbert Krueger
Abstract: The efficacy of anisotropic versus isotropic filtering is analyzed with respect to general phase-based metrics for early vision attributes. We verified that the spectral information content gathered through oriented frequency channels is characterized by high compactness and flexibility, since a wide range of visual attributes emerge from different hierarchical combinations of the same channels. We observed that it is preferable to construct a multichannel, multiorientation representation, rather than using a more compact representation based on an isotropic generalization of the analytic signal. The complete harmonic content is then combined in the phaseorientation space at the final stage, only, to come up with the ultimate perceptual decisions, thus avoiding an “early condensation” of basic features. The resulting algorithmic solutions reach high performance in real-world situations at an affordable computational cost.
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Paper Nr: 207
Title:

A NEW METHOD FOR VIDEO SOCCER SHOT CLASSIFICATION
Authors:

Youness Tabii, Mohamed O. Djibril, Youssef Hadi and Rachid Oulad Haj Thami
Abstract: A shot is often used as the basic unit for both video analysis and indexing. In this paper we present a new method for soccer shot classification on the basis of playfield segmentation. First, we detect the dominant color component, by supposing that playfield pixels are green (dominant color). Second, the segmentation process begins by dividing frames into a 3:5:3 format and then classifying them. The experimental results of our method are very promising, and improve the performance of shot detection.
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Paper Nr: 212
Title:

COLOR MODELS OF SHADOW DETECTION IN VIDEO SCENES
Authors:

Csaba Benedek and Tamás Szirányi
Abstract: In this paper we address the problem of appropriate modelling of shadows in color images. While previous works compared the different approaches regarding their model structure, a comparative study of color models has still missed. This paper attacks a continuous need for defining the appropriate color space for this main surveillance problem. We introduce a statistical and parametric shadow model-framework, which can work with different color spaces, and perform a detailed comparision with it. We show experimental results regarding the following questions: (1) What is the gain of using color images instead of grayscale ones? (2) What is the gain of using uncorrelated spaces instead of the standard RGB? (3) Chrominance (illumination invariant), luminance, or ”mixed” spaces are more effective? (4) In which scenes are the differences significant? We qualified the metrics both in color based clustering of the individual pixels and in the case of Bayesian foreground-background-shadow segmentation. Experimental results on real-life videos show that CIE L*u*v* color space is the most efficient.
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Paper Nr: 264
Title:

TOWARDS OBJECTIVE QUALITY ASSESSMENT OF IMAGE REGISTRATION RESULTS
Authors:

Birgit Moeller, Rafael Garcia and Stefan Posch
Abstract: Geometric registration of visual images is a fundamental intermediate processing step in a wide variety of computer vision applications that deal with image sequence analysis. 2D motion recovery and mosaicing, 3D scene reconstruction and also motion detection approaches strongly rely on accurate registration results. However, automatically assessing the overall quality of a registration is a challenging task. In particular, optimization criteria used in registration are not necessarily closely linked to the final quality of the result and often show a lack of local sensitivity. In this paper we present a new approach for an objective quality metric in 2D image registration. The proposed method is based on local structure analysis and facilitates voting-techniques for error pooling, leading to an objective measure that correlates well with the visual appearance of registered images. Since observed differences are furthermore classified in more detail according to various underlying error sources, the new measure not only yields a suitable base for objective quality assessment, but also opens perspectives towards an automatic and optimally adjusted correction of errors.
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Short Papers
Paper Nr: 38
Title:

FULLY-AUTOMATIC IMPROVEMENT OF THE GEOMETRY OF A VESSEL GRAPH
Authors:

Jan Bruijns, F. J. Peters, R. P. M. Berretty and B. Barenbrug
Abstract: Volume representations of blood vessels acquired by 3D rotational angiography are very suitable for diagnosing a stenosis or an aneurysm. For optimal treatment, physicians need to know the shape parameters of the diseased vessel parts. Therefore, we developed a method for semi-automatic extraction of these parameters from a surface model of the vessel boundaries. To facilitate fully-automatic shape extraction along the vessels, we developed a method to generate a vessel graph. This vessel graph represents the topology faithfully. However, the nodes and the branches are not always located close to the center lines of the vessels. Nodes and branches outside the center region decrease the accuracy of the extracted shape parameters. In this paper we present a method to improve the geometry of a vessel graph.
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Paper Nr: 73
Title:

ROBUST SKYLINE EXTRACTION ALGORITHM FOR MOUNTAINOUS IMAGES
Authors:

Sung W. Yang, Ihn Cheol Kim and Jin Soo Kim
Abstract: Skyline extraction in mountainous images which has been used for navigation of vehicles or micro unmanned air vehicles is very hard to implement because of the complexity of skyline shapes, occlusions by environments, difficulties to detect precise edges and noises in an image. In spite of these difficulties, skyline extraction is a very important theme that can be applied to the various fields of unmanned vehicles applications. In this paper, we developed a robust skyline extraction algorithm using two-scale canny edge images, topological information and location of the skyline in an image. Two-scale canny edge images are composed of High Scale Canny edge image that satisfies good localization criterion and Low Scale Canny edge image that satisfies good detection criterion. By applying each image to the proper steps of the algorithm, we could obtain good performance to extract skyline in images under complex environments. The performance of the proposed algorithm is proved by experimental results using various images and compared with an existing method.
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Paper Nr: 86
Title:

COLOR AND TEXTURE BASED SEGMENTATION ALGORITHM FOR MULTICOLOR TEXTURED IMAGES
Authors:

Irene Fondón, Carmen Serrano and Begoña Acha
Abstract: We propose a color-texture image segmentation algorithm based on multistep region growing. This algorithm is able to deal with multicolored textures. Each of the colors in the texture to be segmented is considered as reference color. In this algorithm color and texture information are extracted from the image by the construction of color distances images, one for each reference color, and a texture energy image. The color distance images are formed by calculating CIEDE2000 distance in the L*a*b* color space to the colors that compound the multicolored texture. The texture energy image is extracted from some statistical moments. The method segment the color information by means of an adaptative N-dimensional region growing where N is the number of reference colors. The tolerance parameter is increased iteratively until an optimum is found and its growth is determined by a step size which depends on the variance on each distance image for the actual grown region. The criterium to decide which is the optimum value of the tolerance parameter depends on the contrast along the edge of the region grown, choosing the one which provides the region with the highest mean contrast in relation to the background. Additionally, this color multistep region growing is texture-controlled, in the sense that an extra condition to include a particular pixel in a region is demanded: the pixel needs to have the same texture as the rest of the pixels within the region. Results prove that the proposed method works very well with general purpose images and significantly improves the results obtained with other previously published algorithm (Fondón et al, 2006).
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Paper Nr: 112
Title:

IMPROVING JUNCTION DETECTION BY SEMANTIC INTERPRETATION
Authors:

Sinan Kalkan, Shi Yan, Florian Pilz and Norbert Krueger
Abstract: Every junction detector has a set of thresholds to make decisions about the junctionness of image points. Low-contrast junctions may pass such thresholds and may not be detected. Lowering the thresholds to find such junctions will lead to spurious junction detections at other image points. In this paper, we implement a junction-regularity measure to improve localization of junctions, and we develop a method to create semantic interpretations of arbitrary junction configurations at improved junction positions. We propose to utilize such a semantic interpretation as a feedback mechanism to filter false-positive junctions. We show results of our proposals on natural images using Harris and SUSAN operators as well as a continuous concept of intrinsic dimensionality.
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Paper Nr: 162
Title:

LOG-UNBIASED LARGE-DEFORMATION IMAGE REGISTRATION
Authors:

Igor Yanovsky, Stanley Osher, Paul M. Thompson and Alex D. Leow
Abstract: In the past decade, information theory has been studied extensively in medical imaging. In particular, image matching by maximizing mutual information has been shown to yield good results in multi-modal image registration. However, there has been few rigorous studies to date that investigate the statistical aspect of the resulting deformation fields. Different regularization techniques have been proposed, sometimes generating deformations very different from one another. In this paper, we apply information theory to quantifying the magnitude of deformations. We examine the statistical distributions of Jacobian maps in the logarithmic space, and develop a new framework for constructing log-unbiased image registration methods. The proposed framework yields both theoretically and intuitively correct deformation maps, and is compatible with large-deformation models. In the results section, we tested the proposed method using pairs of synthetic binary images, two-dimensional serial MRI images, and three-dimensional serial MRI volumes. We compared our results to those computed using the viscous fluid registration method, and demonstrated that the proposed method is advantageous when recovering voxel-wise local tissue change.
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Paper Nr: 182
Title:

3D VOLUME WATERMARKING USING 3D KRAWTCHOUK MOMENTS
Authors:

Athanasios Mademlis, Petros Daras, Dimitrios Tzovaras and Michael Strintzis
Abstract: In this paper a novel blind watermarking method of 3D volumes based on the Weighted 3D Krawtchouk Moments is proposed. The watermark is created by a pseudo-random number generator and is embedded on low order Weighted 3D Krawtchouk Moments. The watermark detection is blind, requiring only the user’s key. The watermark bit sequence is created using the key and its cross correlation with the Weighted 3D Krawtchouk Moments of the possible watermarked volume. The proposed method is imperceivable to the user, robust to geometric transformations (translation, rotation) and to cropping attacks.
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Paper Nr: 187
Title:

FAST SPOT HYPOTHESIZER FOR 2-DE RESEARCH
Authors:

Peter Peer and Luis Galo Corzo
Abstract: Two-dimensional gel electrophoresis (2-DE) images show the expression levels of several hundred of proteins where each protein is represented as a blob shaped spot of grey level values. The spot detection, i.e. segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper we propose a real time spot detector that is basically a morphology based method with use of seeded region growing as a central paradigm and which relies on the spot correlation information. The method is tested on gels with human samples in SWISS-2DPAGE (two-dimensional polyacrylamide gel electrophoresis) database. The average time to process the image is less than a second, while the results are very intuitive for human perception and as such they help the user to focus on important parts of the gel in the subsequent processing. In gels with less than 50 identified spots as proteins (proteins that compose a proteome) in the mentioned database, the algorithm detects all obvious spots.
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Paper Nr: 196
Title:

A COMPARISION OF MODEL-BASED METHODS FOR KNEE CARTILAGE SEGMENTATION
Authors:

James Cheong, Nathan Faggian, Georg Langs, David Suter and Flavia Cicuttini
Abstract: Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient’s knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current “gold standard” (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.
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Paper Nr: 210
Title:

MOTION BLUR ESTIMATION AT CORNERS
Authors:

Giacomo Boracchi and Vincenzo Caglioti
Abstract: In this paper we propose a novel algorithm to estimate motion parameters from a single blurred image, exploiting geometrical relations between image intensities at pixels of a region that contains a corner. Corners are significant both for scene and motion understanding since they permit a univocal interpretation of motion parameters. Motion parameters are estimated locally in image regions, without assuming uniform blur on image so that the algorithm works also with blur produced by camera rotation and, more in general, with space variant blur.
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Paper Nr: 210
Title:

MOTION BLUR ESTIMATION AT CORNERS
Authors:

Giacomo Boracchi and Vincenzo Caglioti
Abstract: In this paper we propose a novel algorithm to estimate motion parameters from a single blurred image, exploiting geometrical relations between image intensities at pixels of a region that contains a corner. Corners are significant both for scene and motion understanding since they permit a univocal interpretation of motion parameters. Motion parameters are estimated locally in image regions, without assuming uniform blur on image so that the algorithm works also with blur produced by camera rotation and, more in general, with space variant blur.
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Paper Nr: 250
Title:

GENETIC ALGORITHM FOR SUMMARIZING NEWS STORIES
Authors:

Mehdi Ellouze, Hichem Karray and Adel M. Alimi
Abstract: This paper presents a new approach summarizing broadcast news using Genetic Algorithms. We propose to segment the news programs into stories, and then summarize stories by selecting from every one of them frames considered important to obtain an informative pictorial abstract. The summaries can help viewers to estimate the importance of the news video. Indeed, by consulting stories summaries we can affirm if the news video contain desired topics.
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Paper Nr: 265
Title:

COMPARATIVE STUDY OF CONTOUR FITTING METHODS IN SPECKLED IMAGES
Authors:

Maria E. Buemi, Maria J. Gambini, Julio C. Jacobo, Marta E. Mejail and Alejandro Frery
Abstract: Images obtained with the use of coherent illumination are affected by a noise called speckle, which is inherent to this type of imaging systems. In this work, speckled data have been statistically treated with a multiplicative model using the family of G distributions. One of the parameters of these distributions can be used to characterize the different degrees of roughness found in speckled data. We used this information to find boundaries between different regions within the image. Two different region contour detection methods for speckled imagery, are presented and compared. The first one maximizes a likelihood function over the speckled data and the second one uses anisotropic difussion over roughness estimates. To represent detected contours, the B-Spline curve representation is used. In order to compare the behaviour of the two methods we performed a Monte Carlo experience. It consisted of the generation of a set of test images with a randomly shaped region, which is considered in the literature as a difficult contour to fit. Then, the mean square error was calculated for each test image, for both methods.
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Paper Nr: 280
Title:

IMPROVED ADAPTIVE BINARIZATION TECHNIQUE FOR DOCUMENT IMAGE ANALYSIS
Authors:

Lal Chandra, Puja Lal, Raju Gupta, Arun Tayal and Dinesh Ganotra
Abstract: Technology of image capturing devices has graduated from Black & White (B&W) to Color, still majority of document image analysis and extraction functionalities work on B&W documents only. Quality of document images directly scanned as B&W is not good enough for further analysis. Moreover, nowadays documents are getting more and more complex with use of variety of background schemes, color combinations and light text on dark background (reverse video) etc. Hence an efficient binarization algorithm becomes an integral step of preprocessing stage. In proposed algorithm we have modified Adaptive Niblack's Method (Rais et al., 2004) of thresholding to make it more efficient and handle reverse video cases also. The proposed algorithm is fast and invariant of factors involved in thresholding of document images like ambient illumination, contrast stretch and shading effects. We have also used gamma correction before applying the proposed binarization algorithm. This gamma correction is adaptive to brightness of document image and is found from predetermined equation of brightness versus gamma. Based upon result of experiments, an optimal size of window for local binarization scheme is also proposed.
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Paper Nr: 284
Title:

INTELLIGENT TOPOLOGY PRESERVING GEOMETRIC DEFORMABLE MODEL
Authors:

Renato Dedic and Madjid Allili
Abstract: Geometric deformable models (GDM) using the level sets method provide a very efficient framework for image segmentation. However, the segmentation results provided by these models are dependent on the contour initialization. Moreover, sometimes it is necessary to prevent the contours from splitting and merging in order to preserve topology. In this work, we propose a new method that can detect the correct boundary information of segmented objects while preserving topology when needed. We adapt the stoping function g in a way that allows us to control the contours’ topology. By analyzing the region where the edges of the contours are close we decide if the contours should merge, split or remain the way they are. This new formulation maintains the advantages of standard (GDM). Moreover,the topology-preserving constraint is enforced efficiently therefore, the new algorithm is only slightly computationally slower over standard (GDM).
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Paper Nr: 307
Title:

BRANCHES FILTERING APPROACH FOR MAX-TREE
Authors:

I. E. Purnama, Gijbertus J. Verkerke, Albert G. Veldhuizen, Peter M. A. van Ooijen, Jaap Lubbers, Tri A. Sardjono and Gijbertus J. Verkerke
Abstract: A new filtering approach called branches filtering is presented. The filtering approach is applied to the Max-Tree representation of an image. Instead of applying filtering criteria to all nodes of the tree, this approach only evaluate the leaf nodes. The expected objects can be found by collecting a number of parent nodes of the selected leaf nodes. The more parent nodes involve the wider the area of the expected objects. The maximum value of the number of parents (PLmax) can be determined by inspecting the output image before having unexpected image. Different images have found have different PLmax values. The branches filtering approach is suitable to extract objects in a noisy image as long as these objects can be recognised from its prominent information such as intensity, shape, or other scalar or vector values. Furthermore, the optimum result can be achieved if the areas which have the prominent information are present in the leaf nodes. The experiments to extract bacteria from noisy image, localizing bony parts in a speckled ultrasound image, and acquiring certain features from a natural image appeared to be feasible give the expected results. The application of the branches filtering approach to a 3D MRA image of human brain to extract the blood vessels gave also the expected image. The results show that the branches filtering can be used as an alternative filtering approach to the original filtering approach of Max-Tree.
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Paper Nr: 311
Title:

LEFT VENTRICLE IMAGE LANDMARKS EXTRACTION USING SUPPORT VECTOR MACHINES
Authors:

Miguel Vera and Antonio Bravo
Abstract: This paper introduces an approach for efficient myocardial landmarks detection in angiograms. Several anatomical landmarks located on the left ventricle are obtained by mean of a support vector machine. Training set corresponds a dataset of landmark and non-landmark 31×31 pixel patterns. Our support vector machine uses the structural risk minimization principle as inference rule and radial basis function kernel. In the training phase false positives were not registered and in the detection phase 100% of recognition was obtained.
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Paper Nr: 312
Title:

GEOMETRIC AND INFORMATION CONSTRAINTS FOR AUTOMATIC LANDMARK SELECTION IN COLPOSCOPY SEQUENCES
Authors:

Juan David Garcia-arteaga, Jan Kybic, Jia Gu and Wenjing Li
Abstract: Colposcopy is a diagnostic method to visually detect cancerous and pre-cancerous tissue regions in the uterine cervix. A typical result is a sequence of cervical images captured at different times after the application of a contrast agent that must be spatially registered to compensate for patient, camera and tissue movement and on which progressive color and texture changes may be seen. We present a method to automatically select correct landmarks for non-consecutive sequence frames captured at long time intervals from a group of candidate matches. Candidate matches are extracted by detecting and matching feature points in consecutive images. Selection is based on geometrical constraints and a local rigid registration using Mutual Information. The results show that these landmarks may be subsequently used to either guide or evaluate the registration of these type of images.
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Paper Nr: 323
Title:

MULTIDIMENSIONAL WAVELET ANALYSIS FOR RECOGNITION OF LESIONS IN COLPOSCOPY TEST
Authors:

Diana T. López, Aldrin Barreto Flores and Leopoldo Altamirano
Abstract: Cervical cancer is an important worldwide disease due the high rate of incidence in the population. Colposcopy is one of the diagnostic tests employed in recognition of lesions, which performs a visual examination of the cervix based on temporal reaction of the surface stained with acetic acid. It is proposed in this paper to evaluate the temporal texture changes produced by the acetic acid based on the concept of the wavelet-aggregated signal in order to identify lesions. An aggregated signal is a scalar signal providing maximum information on the most general variations present in all the processes analyzed and at the same time suppressing components that are characteristic of individual processes. Texture metrics based on spatial information are used in order to analyze temporally the acetic acid response and deduce appropriate signatures. Later, temporal information is analyzed using multidimensional wavelet analysis for identification of lesions.
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Paper Nr: 324
Title:

IMAGE MATTING USING SVM AND NEIGHBORING INFORMATION
Authors:

Tadaaki Hosaka, Takumi Kobayashi and Nobuyuki Otsu
Abstract: Image matting is a technique for extracting a foreground object in a static image by estimating the opacity at each pixel in the foreground image layer. This problem has recently been studied in the framework of optimizing a cost function. The common drawback of previous approaches is the decrease in performance when the foreground and background contain similar colors. To solve this problem, we propose a cost function considering not only a single pixel but also its neighboring pixels, and utilizing the SVM classifier to enhance the discrimination between the foreground and background. Optimization of the cost function can be achieved by belief propagation. Experimental results show favorable matting performance for many images.
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Paper Nr: 335
Title:

TWO-LEVEL METHOD FOR 3D NON-RIGID REGISTRATION - With an Application to Statistical Atlases Construction
Authors:

C. Wu, Patrica E. Murtha, Andy B. Mor and Branko Jaramaz
Abstract: We propose a two-level method for 3D non-rigid registration and apply the method to the problem of building statistical atlases of 3D anatomical structures. 3D registration is an important problem in computer vision and a challenge topic in medical image field due to the geometrical complexity of anatomical shapes and size of medical image data. In this work we adopt a two-level strategy to deal with these problems. Compared with a general multi-resolution framework, we use an interpolation to propagate the matching instead of repeating registration scheme in each resolution. Our algorithm is divided into two main parts: a low-resolution solution to the correspondences and mapping of surface models using Chui and Rangarajan’s robust point matching algorithm, followed by an interpolation to achieve high-resolution correspondences. Experimental results demonstrate our approach for solving the non-rigid registration and correspondences within complicated 3D data sets. In this paper we present an example of this method in the construction of a statistical atlas of the femur.
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Paper Nr: 352
Title:

REMOVING THE TEXTURE FEATURE RESPONSE TO OBJECT BOUNDARIES
Authors:

Padraig Corcoran and Adam Winstanley
Abstract: Texture is a spatial property and thus any features used to describe it must be calculated within a neighbourhood. This process of integrating information over a neighbourhood leads to what we will refer to as the texture boundary response problem, where an unwanted response is observed at object boundaries. This response is due to features being extracted from a mixture of textures and/or an intensity edge between objects. If segmentation is performed using these raw features this will lead to the generation of unwanted classes along object boundaries. To overcome this, post processing of feature images must be performed to remove this response before a classification algorithm can be applied. To date this problem has received little attention with no evaluation of the alternative solutions available in the literature of which we are aware. In this work we perform an evaluation of known solutions to the boundary response problem and discover separable median filtering to be the current best choice. An in depth evaluation of the separable median filtering approach shows that it fails to remove certain parts or types of object boundary response. To overcome this failing we propose two alternative techniques which involve either post processing of the separable median filtered result or an alternative filtering technique.
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Paper Nr: 19
Title:

AUTOMATED IMAGE ANALYSIS OF NOISY MICROARRAYS
Authors:

Sharon Greenblum, Max Krucoff, Jacob Furst and Daniela Raicu
Abstract: A recent extension of DNA microarray technology has been its use in DNA fingerprinting. Our research involved developing an algorithm that automatically analyzes microarray images by extracting useful information while ignoring the large amounts of noise. Our data set consisted of slides generated from DNA strands of 24 different cultures of anthrax from isolated locations (all the same strain that differ only in origin-specific neutral mutations). The data set was provided by Argonne National Laboratories in Illinois. Here we present a fully automated method that classifies these isolates at least as well as the published AMIA (Automated Microarray Image Analysis) Toolbox for MATLAB with virtually no required user interaction or external information, greatly increasing efficiency of the image analysis.
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Paper Nr: 19
Title:

AUTOMATED IMAGE ANALYSIS OF NOISY MICROARRAYS
Authors:

Sharon Greenblum, Max Krucoff, Jacob Furst and Daniela Raicu
Abstract: A recent extension of DNA microarray technology has been its use in DNA fingerprinting. Our research involved developing an algorithm that automatically analyzes microarray images by extracting useful information while ignoring the large amounts of noise. Our data set consisted of slides generated from DNA strands of 24 different cultures of anthrax from isolated locations (all the same strain that differ only in origin-specific neutral mutations). The data set was provided by Argonne National Laboratories in Illinois. Here we present a fully automated method that classifies these isolates at least as well as the published AMIA (Automated Microarray Image Analysis) Toolbox for MATLAB with virtually no required user interaction or external information, greatly increasing efficiency of the image analysis.
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Paper Nr: 107
Title:

A LOOPY BELIEF PROPAGATION APPROACH TO THE SHAPE FROM SHADING PROBLEM
Authors:

Markus Louw, Fred Nicolls and Dee Bradshaw
Abstract: This paper describes a new approach to the shape from shading problem, using loopy belief propagation which is simple and intuitive. The algorithm is called Loopy Belief Propagation Shape-From-Shading (LBP-SFS). It produces reasonable results on real and synthetic data, and surface information from sources other than the image (eg range or stereo data) can be readily incorporated as prior information about the surface elevation at any point, using this framework. In addition, this algorithm proves the use of linear interpolation at the message passing level within a loopy Bayesian network, which to the authors’ knowledge has not been previously explored.
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Paper Nr: 138
Title:

OPTIMAL SPANNING TREES MIXTURE BASED PROBABILITY APPROXIMATION FOR SKIN DETECTION
Authors:

Sanaa El Fkihi, Mohamed Daoudi and D. Aboutajdine
Abstract: In this paper we develop a new skin detection algorithm for learning in color images. Our contribution is based on the Optimal Spanning Tree distributions that are widely used in many optimization areas. Thus, by making some assumptions we propose the mixture of the Optimal Spanning Trees to approximate the true Skin (or Non-Skin) class probability in a supervised algorithm. The theoretical proof of the Optimal Spanning Trees’ mixture is drawn. Furthermore, the performance of our method is assessed on the Compaq database by measuring the Receiver Operating Characteristic curve and its under area. These measures have proved better results of the proposed model compared with the results of a random Optimal Spanning Tree model and the baseline one.
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Paper Nr: 200
Title:

ACCURATE IMAGE REGISTRATION BY COMBINING FEATURE-BASED MATCHING AND GLS-BASED MOTION ESTIMATION
Authors:

Raul Montoliu and Filiberto Pla Bañon
Abstract: In this paper, an accurate Image Registration method is presented. It combines a feature-based method, which allows to recover large motion magnitudes between images, with a Generalized Least-Squares (GLS) motion estimation technique which is able to estimate motion parameters in an accurate manner. The feature-based method gives an initial estimation of the motion parameters, which will be refined using the GLS motion estimator. Our approach has been tested using challenging real images using both affine and projective motion models.
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Paper Nr: 223
Title:

SHAPE COMPARISON OF FLEXIBLE OBJECTS - Similarity of Palm Silhouettes
Authors:

Leonid Mestetskiy
Abstract: We consider the problem of shape comparison for elastic objects presented by binary bitmaps. Our approach to similarity measuring of such objects is based on the conception of a flexible object. A flexible object is defined as a planar graph with a family of circles centered on graph edges. A set of admissible deformations is connected with each flexible object. These deformations are described as a group of planar graph vertices transforms. We define the flexible objects similarity through matching and alignment within the group of admissible deformations. The regular method for approximation of the binary bitmap shape by the flexible object is presented. The flexible object is designed as a subgraph of continuous skeleton of the binary bitmap. The proposed approach is applied to a problem of palm shape recognition for personal biometrical identification.
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Paper Nr: 228
Title:

DISTINGUISHING LIQUID AND VISCOUS BLACK INKS USING RGB COLOUR SPACE
Authors:

Dasari Haritha and Chakravarthy Bhagvati
Abstract: Analysis of inks on Questioned documents is often required in the field of document examination. This paper provides a novel approach for ink type recognition for black inks. Ink types Liquid ink or Viscous ink will be derived from the colour properties of ink, by extracting its amount of blackness. This classification helps in distinguishing Gel and Roller pens versus Ball pens. Different types of inks exhibit different absorption characteristics that causes colour and distribution of colour pixels to change. We observed that the RGB colour space is useful to reveal the differences in ink types. We used multiple linear regression to model the RGB data points of the writings to a plane. The distance from the origin (pure black) to that plane is calculated to classify inks i.e. liquid inks and viscous inks. The distance measures in RGB and HSV colour spaces are used to identify the particular ink. The accuracy of identification is analysed using Type I and Type II errors.
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Paper Nr: 237
Title:

PROBABILISTIC MODELING AND FUSION FOR IMAGE FEATURE EXTRACTION WITH APPLICATIONS TO LICENSE PLATE DETECTION
Authors:

Rami Al-hmouz, Subhash Challa and Duc Vo
Abstract: The paper proposes a novel feature fusion concept for object extraction. The image feature extraction process is modeled as a feature detection problem in noise. The geometric features are probabilistically modeled and detected under various detection thresholds. These detection results are then fused within the Bayesian framework to obtain the final features for further processing. Along with a probabilistic model, pixels voting algorithm is also tested through binary threshold variation. The performance of these approaches is compared with the traditional approaches of image feature extraction in the context of automatic license plate detection problem.
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Paper Nr: 238
Title:

AN ADAPTIVE REGION GROWING SEGMENTATION FOR BLOOD VESSEL DETECTION FROM RETINAL IMAGES
Authors:

Md. Alauddin Bhuiyan, Baikunth Nath and Joselito Chua
Abstract: Blood vessel segmentation from the retinal images is extremely important for assessing retinal abnormalities. A good amount of research has been reported on blood vessel segmentation, but significant improvement is still a necessity particularly on minor vessel segmentation. As the local contrast of blood vessels is unstable (intensity variation), especially in unhealthy retinal images, it becomes very complicated to detect the vessels from the retinal images. In this paper, we propose an edge based vessel segmentation technique to overcome the problem of large intensity variation between major and minor vessels. The edge is detected by considering the adaptive value of gradient employing Region Growing Algorithm, from where parallel edges are computed to select vessels. Our proposed method is efficient and performs well in detecting blood vessels including minor vessels.
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Paper Nr: 256
Title:

HUMAN EYE LOCALIZATION USING EDGE PROJECTIONS
Authors:

Mehmet Turkan, Montse Pardas and A. E. Cetin
Abstract: In this paper, a human eye localization algorithm in images and video is presented for faces with frontal pose and upright orientation. A given face region is filtered by a high-pass filter of a wavelet transform. In this way, edges of the region are highlighted, and a caricature-like representation is obtained. After analyzing horizontal projections and profiles of edge regions in the high-pass filtered image, the candidate points for each eye are detected. All the candidate points are then classified using a support vector machine based classifier. Locations of each eye are estimated according to the most probable ones among the candidate points. It is experimentally observed that our eye localization method provides promising results for both image and video processing applications.
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Paper Nr: 300
Title:

AN ACCURATE ALGORITHM FOR AUTOMATIC STITCHING IN ONE DIMENSION
Authors:

Hitesh Ganjoo, Venkateswarlu Karnati, Pramod Kumar and Raju Gupta
Abstract: The paper addresses the issues in accuracy of various image-stitching algorithms used in the industry today on different types of real-time images. Our paper proposes a stitching algorithm for stitching images in one dimension. The most robust image stitching algorithms make use of feature descriptors to achieve invariance to image zoom, rotation and exposure change. The use of invariant feature descriptors in image matching and alignment makes them more accurate and reliable for a variety of images under different real-time conditions. We assess the accuracy of one such industrial tool, [AUTOSTICH], for our dataset and its underlying Scale Invariant Feature Transform (SIFT) descriptors. The tool’s performance is low in certain scenarios. Our proposed automatic stitching process can be broadly divided into 3 stages: Feature Point Extraction, Points Refinement, and Image Transformation & Blending. Our approach builds on the underlying way a casual end-user captures images through cameras for panoramic image stitching. We have tested the proposed approach on a variety of images and the results show that the algorithm performs well in all scenarios.
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Paper Nr: 305
Title:

A HARRIS CORNER LABEL ENHANCED MMI ALGORITHM FOR MULTI-MODAL AIRBORNE IMAGE REGISTRATION
Authors:

Xiaofeng Fan, Harvey Rhody and Eli Saber
Abstract: Maximization of Mutual information (MMI) is a method that is used widely for multi-modal image registration. However, classical MMI techniques utilize only regional and/or global statistical information and do not make use of spatial features. Several techniques have been proposed to extend MMI to use spatial information, but have proven to be computationally demanding. In this paper, a new approach is proposed to combine spatial information with MMI by using the Harris Corner Label (HCL) algorithm. We use the HCL based MMI algorithm to accelerate the computation and improve the registration over noisy images. Our results indicate that the HCL based registration technique yields superior performance on multimodal imagery when compared to its classical MMI based counterpart.
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Area 3 - Image Understanding

Full Papers
Paper Nr: 74
Title:

CUED SPEECH HAND SHAPE RECOGNITION - Belief Functions as a Formalism to Fuse SVMs & Expert Systems
Authors:

Alexandra Urankar, Alexandra Urankar, Oya Aran, Lale Akarun and Alice Caplier
Abstract: As part of our work on hand gesture interpretation, we present our results on hand shape recognition. Our method is based on attribute extraction and multiple binary SVM classification. The novelty lies in the fashion the fusion of all the partial classification results are performed. This fusion is (1) more efficient in terms of information theory and leads to more accurate result, (2) general enough to allow other source of information to be taken into account: Each SVM output is transformed to a belief function, and all the corresponding functions are fused together with some other external evidential sources of information.
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Paper Nr: 80
Title:

STATISTICAL ANALYSIS OF SECOND-ORDER RELATIONS OF 3D STRUCTURES
Authors:

Sinan Kalkan, Florentin Woergoetter and Norbert Krueger
Abstract: Algorithmic 3D reconstruction methods like stereopsis or structure from motion fail to extract depth at homogeneous image structures where the human visual system succeeds and is able to estimate depth. In this paper, using chromatic 3D range data, we analyze in which way depth in homogeneous structures is related to the depth at the bounding edges. For this, we first extract the local 3D structure of regularly sampled points, and then, analyze the coplanarity relation between these local 3D structures. We can statistically show that the likelihood to find a certain depth at a homogeneous image patch depends on the distance between the image patch and its edges. Furthermore, we find that this prediction is higher when there is a second edge which is proximate to and coplanar with the first edge. These results allow deriving statistically based prediction models for depth extrapolation into homogeneous image structures. We present initial results of a model that predicts depth based on these statistics.
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Paper Nr: 98
Title:

MOTION INFORMATION COMBINATION FOR FAST HUMAN ACTION RECOGNITION
Authors:

Hongying Meng, Nick Pears and Chris Bailey
Abstract: In this paper, we study the human action recognition problem based on motion features directly extracted from video. In order to implement a fast human action recognition system, we select simple features that can be obtained from non-intensive computation. We propose to use the motion history image (MHI) as our fundamental representation of the motion. This is then further processed to give a histogram of the MHI and the Haar wavelet transform of the MHI. The processed MHI thus allows a combined feature vector to be computed cheaply and this has a lower dimension than the original MHI. Finally, this feature vector is used in a SVM-based human action recognition system. Experimental results demonstrate the method to be efficient, allowing it to be used in real-time human action classification systems.
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Paper Nr: 130
Title:

AN IMAGE BASED FEATURE SPACE AND MAPPING FOR LINKING REGIONS AND WORDS
Authors:

Jiayu Tang and Paul Lewis
Abstract: We propose an image based feature space and define a mapping of both image regions and textual labels into that space. We believe the embedding of both image regions and labels into the same space in this way is novel, and makes object recognition more straightforward. Each dimension of the space corresponds to an image from the database. The coordinates of an image segment(region) are calculated based on its distance to the closest segment within each of the images, while the coordinates of a label are generated based on their association with the images. As a result, similar image segments associated with the same objects are clustered together in this feature space, and should also be close to the labels representing the object. The link between image regions and words can be discovered from their separation in the feature space. The algorithm is applied to an image collection and preliminary results are encouraging.
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Paper Nr: 142
Title:

A LEARNING APPROACH TO CONTENT-BASED IMAGE CATEGORIZATION AND RETRIEVAL
Authors:

Washington Mio, Yuhua Zhu and Xiuwen Liu
Abstract: We develop a machine learning approach to content-based image categorization and retrieval. We represent images by histograms of their spectral components associated with a bank of filters and assume that a training database of labeled images – that contains representative samples from each class – is available. We employ a linear dimension reduction technique, referred to as Optimal Factor Analysis, to identify and split off “optimal” low-dimensional factors of the features to solve a given semantic classification or indexing problem. This content-based categorization technique is used to structure databases of images for retrieval according to the likelihood of each class given a query image.
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Paper Nr: 220
Title:

DOCUMENT IMAGE ZONE CLASSIFICATION - A Simple High-Performance Approach
Authors:

Daniel Keysers, Faisal Shafait and Thomas Breuel
Abstract: We describe a simple, fast, and accurate system for document image zone classification — an important sub-problem of document image analysis — that results from a detailed analysis of different features. Using a novel combination of known algorithms, we achieve a very competitive error rate of 1.46% (n = 13811) in comparison to (Wang et al., 2006) who report an error rate of 1.55% (n = 24177) using more complicated techniques. The experiments were performed on zones extracted from the widely used UW-III database, which is representative of images of scanned journal pages and contains ground-truthed real-world data.
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Paper Nr: 221
Title:

2DOF POSE ESTIMATION OF TEXTURED OBJECTS WITH ANGULAR COLOR COOCCURRENCE HISTOGRAMS
Authors:

Thomas Nierobisch and Frank Hoffmann
Abstract: Robust techniques for pose estimation are essential for robotic manipulation and grasping tasks. We present a novel approach for 2DOF pose estimation based on angular color cooccurrence histograms and its application to object grasping. The representation of objects is based on pixel cooccurrence histograms extracted from the color segmented image. The confidence in the pose estimate is predicted by a probabilistic neural network based on the disambiguity of the underlying matchvalue curve. In an experimental evaluation the estimated pose is used as input to the open loop control of a robotic grasp. For more complex manipulation tasks the 2DOF estimate provides the basis for the initialization of a 6DOF geometric based object tracking in real-time.
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Paper Nr: 233
Title:

INFORMATION FUSION TECHNIQUES FOR AUTOMATIC IMAGE ANNOTATION
Authors:

Filippo Vella and Chin-hui Lee
Abstract: Many recent techniques in Automatic Image Annotation use a description of image content based on visual symbolic elements associating textual labels through symbolic connection techniques. These symbolic visual elements, called visual terms, are obtained by a tokenization process starting from the values of features extracted from the training images data set. An interesting issue for this approach is to exploit, through information fusion, the representations with visual terms derived by different image features. We show techniques for the integration of visual information from different image features and compare the results achieved by them.
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Paper Nr: 241
Title:

FAST AND ROBUST IMAGE MATCHING USING CONTEXTUAL INFORMATION AND RELAXATION
Authors:

Desire Sidibe, Philippe Montesinos and Stefan Janaqi
Abstract: This paper tackles the difficult, but fundamental, problem of image matching under projective transformation. Recently, several algorithms capable of handling large changes of viewpoint as well as large scale changes have been proposed. They are based on the comparison of local, invariants descriptors which are robust to these transformations. However, since no image descriptor is robust enough to avoid mismatches, an additional step of outliers rejection is often needed. The accuracy of which strongly depends on the number of mismatches. In this paper, we show that the matching process can be made robust to ensure a very few number of mismatches based on a relaxation labeling technique. The main contribution of this work is in providing an efficient and fast implementation of a relaxation method which can deal with large sets of features. Futhermore, we show how the contextual information can be obtained and used in this robust and fast algorithm. Experiments with real data and comparison with other matching methods, clearly show the improvements in the matching results.
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Paper Nr: 281
Title:

BIASED MANIFOLD EMBEDDING FOR PERSON-INDEPENDENT HEAD POSE ESTIMATION
Authors:

Vineeth N. Balasubramanian and Sethuraman Panchanathan
Abstract: Head pose estimation is an integral component of face recognition systems and human computer interfaces. To determine the head pose, face images with varying pose angles can be considered to lie on a smooth low-dimensional manifold in high-dimensional feature space. In this paper, we propose a novel supervised approach to manifold-based non-linear dimensionality reduction for head pose estimation. The Biased Manifold Embedding method is pivoted on the ideology of using the pose angle information of the face images to compute a biased geodesic distance matrix, before determining the low-dimensional embedding. A Generalized Regression Neural Network (GRNN) is used to learn the non-linear mapping, and linear multi-variate regression is finally applied on the low-dimensional space to obtain the pose angle. We tested this approach on face images of 24 individuals with pose angles varying from -90◦ to +90◦ with a granularity of 2◦ . The results showed significant reduction in the error of pose angle estimation, and robustness to variations in feature spaces, dimensionality of embedding and other parameters.
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Short Papers
Paper Nr: 85
Title:

PRACTICAL SINGLE VIEW METROLOGY FOR CUBOIDS
Authors:

Nick Pears, Paul Wright and Chris Bailey
Abstract: Generally it is impossible to determine the size of an object from a single image due to the depth-scale ambiguity problem. However, with knowledge of the geometry of the scene and the existence of known reference dimensions in the image, it is possible to infer the real world dimensions of objects with only a single image. In this paper, we investigate different methods of automatically determining the dimensions of cuboids (rectangular boxes) from a single image, using a novel reference target. In particular, two approaches will be considered: the first will use the cross-ratio projective invariant and the other will use the planar homography. The accuracy of the measurements will be evaluated in the presence of noise in the feature points. The effects of lens distortions on the accuracy of the measurements will be investigated. Automatic feature detection techniques will also be considered.
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Paper Nr: 93
Title:

AN INTERPOLATION METHOD FOR THE RECONSTRUCTION AND RECOGNITION OF FACE IMAGES
Authors:

Ngoc C. Nguyen and Jaume Peraire
Abstract: An interpolation method is presented for the reconstruction and recognition of human face images. Basic ingredients include an optimal basis set defining a low-dimensional face space and a set of interpolation points capturing the most relevant characteristics of known faces. The interpolation points are chosen as pixels of the pixel grid so as to best interpolate the set of known face images. These points are then used in a least-squares interpolation procedure to determine interpolant components of a face image very inexpensively, thereby providing efficient reconstruction of faces. In addition, the method allows a fully automatic computer system to be developed for the real-time recognition of faces. The advantages of this method are: (1) the computational cost of recognizing a new face is independent of the size of the pixel grid; and (2) it allows for the reconstruction and recognition of incomplete images.
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Paper Nr: 115
Title:

ACTIVE OBJECT DETECTION
Authors:

Guido De Croon
Abstract: We investigate an object-detection method that employs active image scanning. The method extracts a local sample at the current scanning position and maps it to a shifting vector indicating the next scanning position. The method’s goal is to move the scanning position to an object location, skipping regions in the image that are unlikely to contain an object. We apply the active object-detection method (AOD-method) to a face-detection task and compare it with window-sliding object-detection methods, which employ passive scanning. We conclude that the AOD-method performs at par with these methods, while being computationally less expensive. In a conservative estimate the AOD-method extracts 45 times fewer local samples, leading to a 50% reduction of computational effort. This reduction is obtained at the expense of application generality.
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Paper Nr: 146
Title:

ADAPTIVE DOCUMENT BINARIZATION - A Human Vision Approach
Authors:

Vassilios Vonikakis, Ioannis Andreadis, Nikos Papamarkos and Antonios Gasteratos
Abstract: This paper presents a new approach to adaptive document binarization, inspired by the attributes of the Human Visual System (HVS). The proposed algorithm combines the characteristics of the OFF ganglion cells of the HVS with the classic Otsu binarization technique. Ganglion cells with four receptive field sizes tuned to different spatial frequencies are employed, which, adopting a new activation function, are independent of gradual illumination changes, such as shadows. The Otsu technique is then used for thresholding the outputs of the ganglion cells, resulting to the final segmentation of the characters from the background. The proposed method was quantitatively and qualitatively tested against other contemporary adaptive binarization techniques in various shadow levels and noise densities, and it was found to outperform them.
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Paper Nr: 178
Title:

SIMULTANEOUS REGISTRATION AND CLUSTERING FOR TEMPORAL SEGMENTATION OF FACIAL GESTURES FROM VIDEO
Authors:

Fernando De La Torre, Alvaro Collet, Jeff Cohn and Takeo Kanade
Abstract: Temporal segmentation of facial gestures from video sequences is an important unsolved problem for automatic facial analysis. Recovering temporal gesture structure from a set of 2D facial features tracked points is a challenging problem because of the difficulty of factorizing rigid and non-rigid motion and the large variability in the temporal scale of the facial gestures. In this paper, we propose a two step approach for temporal segmentation of facial gestures. The first step consist on clustering shape and appearance features into a number of clusters and the second step involves temporally grouping these clusters. Results on clustering largely depend on the registration process. To improve the clustering/registration, we propose a Parameterized Cluster Analysis (PaCA) method that jointly performs registration and clustering. Besides the joint clustering/registration, PaCA solves the rounding off problem of existing spectral graph methods for clustering. After the clustering is performed, we group sets of clusters into facial gestures. Several toy and real examples show the benefits of our approach for temporal facial gesture segmentation.
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Paper Nr: 179
Title:

PARAMETERIZED KERNELS FOR SUPPORT VECTOR MACHINE CLASSIFICATION
Authors:

Fernando De La Torre and Oriol Vinyals
Abstract: Kernel machines (e.g. SVM, KLDA) have shown state-of-the-art performance in several visual classification tasks. The classification performance of kernel machines greatly depends on the choice of kernels and its parameters. In this paper, we propose a method to search over the space of parameterized kernels using a gradient-based method. Our method effectively learns a non-linear representation of the data useful for classification and simultaneously performs dimensionality reduction. In addition, we introduce a new matrix formulation that simplifies and unifies previous approaches. The effectiveness and robustness of the proposed algorithm is demonstrated in both synthetic and real examples of pedestrian and mouth detection in images.
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Paper Nr: 189
Title:

CATEGORY LEVEL OBJECT SEGMENTATION - Learning to Segment Objects with Latent Aspect Models
Authors:

Diane Larlus and Frédéric Jurie
Abstract: We propose a new method for learning to segment objects in images. This method is based on a latent variables model used for representing images and objects, inspired by the LDA model. Like the LDA model, our model is capable of automatically discovering which visual information comes from which object. We extend LDA by considering that images are made of multiple overlapping regions, treated as distinct documents, giving more chance to small objects to be discovered. This model is extremely well suited for assigning image patches to objects (even if they are small), and therefore for segmenting objects. We apply this method on objects belonging to categories with high intra-class variations and strong viewpoint changes.
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Paper Nr: 214
Title:

DETECTION OF PERFECT AND APPROXIMATE REFLECTIVE SYMMETRY IN ARBITRARY DIMENSION
Authors:

Darko Dimitrov and Klaus Kriegel
Abstract: Symmetry detection is an important problem with many applications in pattern recognition, computer vision and computational geometry. In this paper, we propose a novel algorithm for computing a hyperplane of reflexive symmetry of a point set in arbitrary dimension with approximate symmetry. The algorithm is based on the geometric hashing technique. In addition, we consider a relation between the perfect reflective symmetry and the principal components of shapes, a relation that was already a base of few heuristic approaches that tackle the symmetry problem in 2D and 3D. From mechanics, it is known that, if H is a plane of reflective symmetry of the 3D rigid body, then a principal component of the body is orthogonal to H . Here we extend that result to any point set (continuous or discrete) in arbitrary dimension.
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Paper Nr: 222
Title:

DETECTING AND CLASSIFYING FRONTAL, BACK AND PROFILE VIEWS OF HUMANS
Authors:

Narayanan C. Krishnan, Baoxin Li and Sethuraman Panchanathan
Abstract: Detecting and estimating the presence and pose of a person in an image is a challenging problem. Literature has dealt with this as two separate problems. In this paper, we propose a system that introduces novel steps to segment the foreground object from the back ground and classifies the pose of the detected human as frontal, profile or back view. We use this as a front end to an intelligent environment we are developing to assist individuals who are blind in office spaces. The traditional background subtraction often results in silhouettes that are discontinuous, containing holes. We have incorporated the graph cut algorithm on top of background subtraction result and have observed a significant improvement in the performance of segmentation yielding continuous silhouettes without any holes. We then extract shape context features from the silhouette for training a classifier to distinguish between profile and nonprofile(frontal or back) views. Our system has shown promising results by achieving an accuracy of 87.5% for classifying profile and non profile views using an SVM on the real data sets that we have collected for our experiments.
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Paper Nr: 227
Title:

INTEGRATED GLOBAL AND OBJECT-BASED IMAGE RETRIEVAL USING A MULTIPLE EXAMPLE QUERY SCHEME
Authors:

Gustavo Borba, Humberto R. Gamba, Liam Mayron and Oge Marques
Abstract: Conventional content-based image retrieval (CBIR) systems typically do not consider the limitations of the feature extraction-distance measurement paradigm when capturing a user’s query. This issue is compounded by the complicated interfaces that are featured by many CBIR systems. The framework proposed in this work embodies new concepts that help mitigate such limitations. The front-end includes an intuitive user interface that allows for fast image organization though spatial placement and scaling. Additionally, a multiple-image query is combined with a region-of-interest extraction algorithm to automatically trigger global or object-based image analysis. The relative scale of the example images are considered to be indicative of image relevance and are also considered during the retrieval process. Experimental results demonstrate promising results.
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Paper Nr: 258
Title:

PARALLEL GABOR PCA WITH FUSION OF SVM SCORES FOR FACE VERIFICATION
Authors:

ÁNgel Serrano Sánchez de León, Cristina Conde, Isaac Martín De Diego, Enrique Cabello, Li Bai and Linlin Shen
Abstract: Here we present a novel fusion technique for support vector machine (SVM) scores, obtained after a dimension reduction with a principal component analysis algorithm (PCA) for Gabor features applied to face verification. A total of 40 wavelets (5 frequencies, 8 orientations) have been convolved with public domain FRAV2D face database (109 subjects), with 4 frontal images with neutral expression per person for the SVM training and 4 different kinds of tests, each with 4 images per person, considering frontal views with neutral expression, gestures, occlusions and changes of illumination. Each set of wavelet-convolved images is considered in parallel or independently for the PCA and the SVM classification. A final fusion is performed taking into account all the SVM scores for the 40 wavelets. The proposed algorithm improves the Equal Error Rate for the occlusion experiment compared to a Downsampled Gabor PCA method and obtains similar EERs in the other experiments with fewer coefficients after the PCA dimension reduction stage.
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Paper Nr: 282
Title:

AUTOMATED STAR/GALAXY DISCRIMINATION IN MULTISPECTRAL WIDE-FIELD IMAGES
Authors:

Jorge De La Calleja and Olac Fuentes
Abstract: In this paper we present an automated method for classifying astronomical objects in multi-spectral widefield images. The classification method is divided into three main stages. The first one consists of locating and matching the astronomical objects in the multi-spectral images. In the second stage we create a compact representation of each object applying principal component analysis to the images. In the last stage we classify the astronomical objects using locally weighted linear regression and a novel oversampling algorithm to deal with the unbalance that is inherent to this class of problems. Our experimental results show that our method performs accurate classification using small training sets and in the presence of significant class unbalance.
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Paper Nr: 283
Title:

MULTIRESOLUTION TEXT DETECTION IN VIDEO FRAMES
Authors:

Marios Anthimopoulos, Basilios Gatos and Ioannis Pratikakis
Abstract: This paper proposes an algorithm for detecting artificial text in video frames using edge information. First, an edge map is created using the Canny edge detector. Then, morphological dilation and opening are used in order to connect the vertical edges and eliminate false alarms. Bounding boxes are determined for every non-zero valued connected component, consisting the initial candidate text areas. Finally, an edge projection analysis is applied, refining the result and splitting text areas in text lines. The whole algorithm is applied in different resolutions to ensure text detection with size variability. Experimental results prove that the method is highly effective and efficient for artificial text detection.
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Paper Nr: 313
Title:

A NOVEL RELEVANCE FEEDBACK PROCEDURE BASED ON LOGISTIC REGRESSION AND OWA OPERATOR FOR CONTENT-BASED IMAGE RETRIEVAL SYSTEM
Authors:

Pedro Zuccarello, Esther De Ves, Teresa León, Guillermo Ayala and Juan Domingo
Abstract: This paper presents a new algorithm for content based retrieval systems in large databases. The objective of these systems is to find the images which are as similar as possible to a user query from those contained in the global image database without using textual annotations attached to the images. The procedure proposed here to address this problem is based on logistic regression model: the algorithm considers the probability of an image to belong to the set of those desired by the user. In this work a relevance proabaility π(I) is a quantity wich reflects the estimate of the relevance of the image I with respect to the user’s preferences. The problem of the small sample size with respect to the number of features is solved by adjusting several partial linear models and combining its relevance probabilitis by means of an ordered averaged weighted operator. Experimental results are shown to evaluate the method on a large image database in term of the average number of iterations needed to find a target image.
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Paper Nr: 329
Title:

FACIAL POSE ESTIMATION FOR IMAGE RETRIEVAL
Authors:

Andreas Savakis and James Schimmel
Abstract: Face detection is a prominent semantic feature which, along with low-level features, is often used for content-based image retrieval. In this paper we present a human facial pose estimation method that can be used to generate additional metadata for more effective image retrieval when a face is already detected. Our computationally efficient pose estimation approach is based on a simplified geometric head model and combines artificial neural network (ANN) detectors with template matching. Testing at various poses demonstrated that the proposed method achieves pose estimation within 4.28 degrees on average, when the facial features are accurately detected.
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Paper Nr: 341
Title:

AN EFFICIENT FUSION STRATEGY FOR MULTIMODAL BIOMETRIC SYSTEM
Authors:

Nitin Agrawal, Hunny Mehrotra, Phalguni Gupta and C. Jinshong Hwang
Abstract: This paper proposes an efficient multi-step fusion strategy for multimodal biometric system. Fusion is done at two stages i.e., algorithm level and modality level. At algorithm level the important steps involved are normalization, data elimination and assignment of static and dynamic weights. Further, the individual recognizers are combined using sum of scores technique. Finally the integrated scores from individual traits are passed to decision module. Fusion at decision level is done using Support Vector Machines (SVM). The SVM is trained by the set of matching scores and it classifies the data into two known classes i.e., genuine and imposters. The system is tested on database collected for 200 individuals and is showing a considerable increase in accuracy (overall accuracy 98.42%) compared to individual traits.
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Paper Nr: 364
Title:

AN APPROXIMATE REASONING TECHNIQUE FOR SEGMENTATION ON COMPRESSED MPEG VIDEO
Authors:

Luis Rodríguez Benitez, Juan Moreno García, Javier Alonso Albusac Jiménez, José J. Castro-Schez and Luis Jiménez Linares
Abstract: In this work we present a system that describes linguistically the position of an object in motion in each frame of a video stream. This description is obtained directly from MPEG motion vectors by using the theory of fuzzy sets and approximate reasoning. The lack of information and noisy data over the compressed domain justifies the use of fuzzy logic. Besides, the use of linguistic labels is necessary since the system’s output is a semantic description of trajectories and positions. Several methods of extraction of motion information from MPEG motion vectors can be found in the revised literature. As no numerical results are given of these methods, we present a statistical study of the input motion information and compare the output of the system depending on the selected extraction technique. For system performance evaluation it would be necessary to determine the error between the semantic output and the desired object’s description. This comparison is carried out between the (x,y) pixel coordinates of the center position of the object and the resulting value of a defuzzification method applied to the description labels. The system has been evaluated using three different video samples of the standard datasets provided by several PETS (Performance Evaluation of Tracking and Surveillance) workshops.
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Paper Nr: 76
Title:

HUMAN VISUAL PERCEPTION, GESTALT PRINCIPLES AND DUALITY REGION-CONTOUR - Application to Computer Image Analysis of Human Cornea Endothelium
Authors:

Yann Gavet, Jean-charles Pinoli, Gilles Thuret and Philippe Gain
Abstract: The human visual system is far more efficient than a computer to analyze images, especially when noise or poor acquisition process make the analysis impossible by lack of information. To mimic the human visual system, we develop algorithms based on the gestalt theory principles: proximity and good continuation. We also introduce the notion of mosaic that we reconstruct with those principles. Mosaics can be defined as geometry figures (squares, triangles), or issued from a contour detection system or a skeletonization process. The application presented here is the detection of cornea endothelial cells. They present a very geometric structure that give enough information for a non expert to be able to perform the same analysis as the ophthalmologist, that mainly consists on counting the cells and evaluating the cell density.
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Paper Nr: 105
Title:

MODIFIED DISTANCE SIGNATURE AS AN ENHANCIVE DESCRIPTOR OF COMPLEX PLANAR SHAPES
Authors:

Andrzej Florek and Tomasz Piascik
Abstract: In this paper, a simple and efficient approach to classify planar shapes is proposed. This approach is based on comparison of areas of dynamicly sampled classic signatures. Presented approach is dedicated to the recognition of convex and concave planar shapes, containing openings in the area enclosed by boundary. A way to calculate the discrete representation of classic distance-versus-angle signatures, a reduction of memory requirements and a number of calculations are presented. Analysis carried out from classification experiments applied to images of real objects (car-engine collector seals) indicates good properties of dissimilarity coefficients, based on modified signature, taken as an object descriptor.
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Paper Nr: 125
Title:

FACE ALIGNMENT USING ACTIVE APPEARANCE MODEL OPTIMIZED BY SIMPLEX
Authors:

Yasser Aidarous, S. L. Gallou, Abdul Sattar and Renaud Seguier
Abstract: The active appearance models (AAM) are robust in face alignment. We use this method to analyze gesture and motions of faces in Human Machine Interfaces (HMI) for embedded systems (mobile phone, game console, PDA: Personal Digital Assistant). However these models are not only high memory consumer but also efficient especially when the aligning objects in the learning data base, which generate model, are imperfectly represented. We propose a new optimization method based on Nelder Mead Simplex (NELDER and MEAD, 1965). The Simplex reduces 73% of memory requirement and improves the efficiency of AAM at the same time. The test carried out on unknown faces (from BioID data base (BioID, )) shows that our proposition provides accurate alignment whereas the classical AAM is unable to align the object.
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Paper Nr: 132
Title:

IMAGE RETRIEVAL WITH BINARY HAMMING DISTANCE
Authors:

Jérôme Landré and Frédéric Truchetet
Abstract: This article proposes a content-based indexing and retrieval (CBIR) system based on query-by-visual-example using hierarchical binary signatures. Binary signatures are obtained through a described binarization process of classical features (color, texture and shape). The Hamming binary distance (based on binary XOR operation) is used for computing distances. This technique was tested on a real natural image collection containing 10 000 images and on a virtual collection of one million images. Results are very good both in terms of speed and accuracy allowing near real-time image retrieval in very large image collections.
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Paper Nr: 133
Title:

PERFORMANCE OF A COMPACT FEATURE VECTOR IN CONTENT-BASED IMAGE RETRIEVAL
Authors:

Gita Das and Sid Ray
Abstract: In this paper, we considered image retrieval as a dichotomous classification problem and studied the effect of sample size and dimensionality on the retrieval accuracy. Finite sample size has always been a problem in Content-Based Image Retrieval (CBIR) system and it is more severe when feature dimension is high. Here, we have discussed feature vectors having different dimensions and their performance with real and synthetic data, with varying sample sizes. We reported experimental results and analysis with two different image databases of size 1000, each with 10 semantic categories.
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Paper Nr: 155
Title:

DETECTION OF FACIAL CHARACTERISTICS BASED ON EDGE INFORMATION
Authors:

Stylianos Asreriadis, Nikolaos Nikolaidis, Ioannis Pitas and Montse Pardas
Abstract: In this paper, a novel method for eye and mouth detection and eye center and mouth corner localization, based on geometrical information is presented. First, a face detector is applied to detect the facial region, and the edge map of this region is extracted. A vector pointing to the closest edge pixel is then assigned to every pixel. x and y components of these vectors are used to detect the eyes and mouth. For eye center localization, intensity information is used, after removing unwanted effects, such as light reflections. For the detection of the mouth corners, the hue channel of the lip area is used. The proposed method can work efficiently on low-resolution images and has been tested on the XM2VTS database with very good results.
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Paper Nr: 194
Title:

HUMAN IDENTIFICATION USING FACIAL CURVES WITH EXTENSIONS TO JOINT SHAPE-TEXTURE ANALYSIS
Authors:

Chafik Samir, Mohamed Daoudi and Anuj Srivastava
Abstract: Recognition of human beings using shapes of their full facial surfaces is a difficult problem. Our approach is to approximate a facial surface using a collection of (closed) facial curves, and to compare surfaces by comparing their corresponding curves. The method is further strengthened by the use of texture maps (video images) associated with these faces. Using the commonly used spectral representation of a texture image, i.e. filter images using Gabor filters and compute histograms as image representations, we can compare texture images by comparing their corresponding histograms using the chi-squared distance. A combination of shape and texture metrics provides a method to compare textured, facial surfaces, and we demonstrate its application in face recognition using 240 facial scans of 40 subjects.
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Paper Nr: 198
Title:

MULTIPLE CLASSIFIERS ERROR RATE OPTIMIZATION APPROACHES OF AN AUTOMATIC SIGNATURE VERIFICATION (ASV) SYSTEM
Authors:

Sharifah S. Ahmad
Abstract: Decision level management is a crucial aspect in an Automatic Signature Verification (ASV) system, due to its nature as the centre of decision making that decides on the validity or otherwise of an input signature sample. Here, investigations are carried out in order to improve the performance of an ASV system by applying multiple classifier approaches, where features of the system are grouped into two different sub- sets, namely static and dynamic subsets, hence having two different classifiers. In this work, three decision fusion methods, namely Majority Voting, Borda Count and cascaded multi-stage cascaded classifiers are analyzed for their effectiveness in improving the error rate performance of the ASV system. The performance analysis is based upon a database that reflects an actual user population in a real application environment, where as the system performance improvement is calculated with respect to the initial system Equal Error Rate (EER) where multiple classifiers approaches were not adopted.
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Paper Nr: 217
Title:

OBJECT RECOGNITION AND POSE ESTIMATION ACROSS ILLUMINATION CHANGES
Authors:

Damien Muselet, Brian Funt, Lilong Shi and Ludovic Macaire
Abstract: In this paper, we present a new algorithm for color-based object recognition that detects objects and estimates their pose (position and orientation) in cluttered scenes observed under uncontrolled illumination conditions. As with so many other color-based object-recognition algorithms, color histograms are also fundamental to our approach; however, we use histograms obtained from overlapping subwindows, rather than the entire image. Furthermore, each local histogram is normalized using greyworld normalization in order to be as less sensitive to illumination as possible. An object from a database of prototype objects is identified and located in an input image by matching the subwindow contents. The prototype is detected in the input whenever many good histogram matches are found between the subwindows of the input image and those of the prototype. In essence, normalized color histograms of subwindows are the local features being matched. Once an object has been recognized, its 2D pose is found by approximating the geometrical transformation most consistently mapping the locations of prototype’s subwindows to their matched subwindow locations in the input image.
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Paper Nr: 260
Title:

AUTOMATIC LIP LOCALIZATION AND FEATURE EXTRACTION FOR LIP-READING
Authors:

Salah Werda, Walid Mahdi and Abdelmajid Ben Hamadou
Abstract: In recent year, lip-reading systems have received a great attention, since it plays an important role in human communication with computer especially for hearing impaired or elderly people. The need for an automatic lip-reading system is ever increasing. Today, extraction and reliable analysis of facial movements make up an important part in many multimedia systems such as videoconference, low communication systems, lip- reading systems. We can imagine, for example, a dependent person ordering a machine with an easy lip movement or by a simple syllable pronunciation. We present in this paper a new approach for lip localization and feature extraction in a speaker’s face. The extracted visual information is then classified in order to recognize the uttered viseme (visual phoneme). To check our system performance we have developed our Automatic Lip Feature Extraction prototype (ALiFE). Experiments revealed that our system recognizes 70.95 % of French digits uttered under natural conditions.
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Paper Nr: 272
Title:

EXTRACTION OF WHEAT EARS WITH STATISTICAL METHODS BASED ON TEXTURE ANALYSIS
Authors:

Mohamed Bakhouche, Frédéric Cointault and Pierre Gouton
Abstract: In the agronomic domain, the simplification of crop counting is a very important and fastidious step for technical institutes such as Arvalis1, which has then proposed us to use image processing to detect the number of wheat ears in images acquired directly in a field. Texture image segmentation techniques based on feature extraction by first and higher order statistical methods have been developped for unsupervised pixel classification. The K-Means algorithm is implemented before the choice of a threshold to highlight the ears. Three methods have been tested with very heterogeneous results, except the run length technique for which the results are closed to the visual counting with an average error of 6%. Although the evaluation of the quality of the detection is visually done, automatic evaluation algorithms are currently implementing. Moreover, other statistical methods of higher order must be implemented in the future jointly with methods based on spatio-frequential transforms and specific filtering.
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Paper Nr: 293
Title:

TRAFFIC SIGN CLASSIFICATION USING ERROR CORRECTING TECHNIQUES
Authors:

Sergio Escalera, Petia Radeva and Oriol Pujol
Abstract: Traffic sign classification is a challenging problem in Computer Vision due to the high variability of sign appearance in uncontrolled environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a classification technique for traffic signs recognition by means of Error Correcting Output Codes. Recently, new proposals of coding and decoding strategies for the Error Correcting Output Codes framework have been shown to be very effective in front of multiclass problems. We review the state-of-the-art ECOC strategies and combinations of problem-dependent coding designs and decoding techniques. We apply these approaches to the Mobile Mapping problem. We detect the sign regions by means of Adaboost. The Adaboost in an attentional cascade with the extended set of Haar-like features estimated on the integral shows great performance at the detection step. Then, a spatial normalization using the Hough transform and the fast radial symmetry is done. The model fitting improves the final classification performance by normalizing the sign content. Finally, we classify a wide set of traffic signs types, obtaining high success in adverse conditions.
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Paper Nr: 293
Title:

TRAFFIC SIGN CLASSIFICATION USING ERROR CORRECTING TECHNIQUES
Authors:

Sergio Escalera, Petia Radeva and Oriol Pujol
Abstract: Traffic sign classification is a challenging problem in Computer Vision due to the high variability of sign appearance in uncontrolled environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a classification technique for traffic signs recognition by means of Error Correcting Output Codes. Recently, new proposals of coding and decoding strategies for the Error Correcting Output Codes framework have been shown to be very effective in front of multiclass problems. We review the state-of-the-art ECOC strategies and combinations of problem-dependent coding designs and decoding techniques. We apply these approaches to the Mobile Mapping problem. We detect the sign regions by means of Adaboost. The Adaboost in an attentional cascade with the extended set of Haar-like features estimated on the integral shows great performance at the detection step. Then, a spatial normalization using the Hough transform and the fast radial symmetry is done. The model fitting improves the final classification performance by normalizing the sign content. Finally, we classify a wide set of traffic signs types, obtaining high success in adverse conditions.
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Paper Nr: 301
Title:

FACE VERIFICATION BY SHARING KNOWLEDGE FROM DIFFERENT SUBJECTS
Authors:

David Masip, Agata Lapedriza Garcia and Jordi Vitria Marca
Abstract: In face verification problems the number of training samples from each class is usually reduced, making difficult the estimation of the classifier parameters. In this paper we propose a new method for face verification where we simultaneously train different face verification tasks, sharing the model parameter space. We use a multi-task extended logistic regression classifier to perform the classification. Our approach allows to share information from different classification tasks (transfer knowledge), mitigating the effects of the reduced sample size problem. Our experiments performed using the publicly available AR Face Database, show lower error rates when multiple tasks are jointly trained sharing information, which confirms the theoretical approx- imations in the related literature.
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Paper Nr: 301
Title:

FACE VERIFICATION BY SHARING KNOWLEDGE FROM DIFFERENT SUBJECTS
Authors:

David Masip, Agata Lapedriza Garcia and Jordi Vitria Marca
Abstract: In face verification problems the number of training samples from each class is usually reduced, making difficult the estimation of the classifier parameters. In this paper we propose a new method for face verification where we simultaneously train different face verification tasks, sharing the model parameter space. We use a multi-task extended logistic regression classifier to perform the classification. Our approach allows to share information from different classification tasks (transfer knowledge), mitigating the effects of the reduced sample size problem. Our experiments performed using the publicly available AR Face Database, show lower error rates when multiple tasks are jointly trained sharing information, which confirms the theoretical approx- imations in the related literature.
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Paper Nr: 317
Title:

EXTERIOR ORIENTATION USING LINE-BASED ROTATIONAL MOTION ANALYSIS
Authors:

Agustin Navarro, Edgar Villarraga and Joan Aranda
Abstract: 3D scene information obtained from a sequence of images is very useful in a variety of action-perception applications. Most of them require perceptual orientation of specific objects to interact with their environment. In the case of moving objects, the relation between changes in image features derived by 3D transformations can be used to estimate its orientation with respect to a fixed camera. Our purpose is to describe some properties of movement analysis over projected features of rigid objects represented by lines, and introduce a line-based orientation estimation algorithm through rotational motion analysis. Experimental results showed some advantages of this new algorithm such as simplicity and real-time performance. This algorithm demonstrates that it is possible to estimate the orientation with only two different rotations, having knowledge of the transformations applied to the object.
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Paper Nr: 319
Title:

AN ATTENTION-BASED METHOD FOR EXTRACTING SALIENT REGIONS OF INTEREST FROM STEREO IMAGES
Authors:

Oge Marques, Liam Mayron, Daniel Socek, Gustavo Borba and Humberto R. Gamba
Abstract: A fundamental problem in computer vision is caused by the projection of a three-dimensional world onto one or more two-dimensional planes. As a result, methods for extracting regions of interest (ROIs) have certain limitations that cannot be overcome with traditional techniques that only utilize a single projection of the image. For example, while it is difficult to distinguished two overlapping, homogeneous regions with a single intensity or color image, depth information can usually easily be used to separate the regions. In this paper we present an extension to an existing saliency-based ROI extraction method. By adding depth information to the existing method many previously difficult scenarios can now be handled. Experimental results show consistently improved ROI segmentation.
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Paper Nr: 343
Title:

PARTS-BASED FACE DETECTION AT MULTIPLE VIEWS
Authors:

Andreas Savakis and David Higgs
Abstract: This paper presents a parts-based approach to face detection, that is intuitive, easy to implement and can be used in conjunction with other image understanding operations that use prominent facial features. Artificial neural networks are trained as view-specific parts detectors for the eyes, mouth and nose. Once these salient facial features are identified, results for each view are integrated through a Bayesian network in order to reach the final decision. System performance is comparable to other state-of the art face detection methods while providing support for different view angles and robustness to partial occlusions.
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Paper Nr: 344
Title:

CORAL REEF TEXTURE CLASSIFICATION USING SUPPORT VECTOR MACHINES
Authors:

Anand Mehtaa, Eraldo Ribeiro, Jessica Gilner and Robert van Woesikb
Abstract: The development of tools to examine the ecological parameters of coral reefs is seriously lagging behind available computer-based technology. Until recently the use of images in environmental and ecological data gathering has been limited to terrestrial analysis because of difficulties in underwater image capture and data analysis. In this paper, we propose the application of computer vision to address the problem of monitoring and classifying coral reef colonies. More specifically, we present a method to classify coral reef images based on their textural appearance using support vector machines (SVM). Our algorithm uses raw pixel color values directly as sample vectors. We show promising results on region classification of three coral types for low quality underwater images. This will allow for more timely analysis of coral reef images and broaden the capabilities of underwater data interpretation.
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Rejecteds
Paper Nr: 149
Title:

IMPROVED FACE RECOGNITION USING KERNEL DIRECT DISCRIMINANT ANALYSIS IN COMBINATION WITH SVM CLASSIFIER
Authors:

Seyyedmajid Valiollahzadeh, Abolgasem Sayadiyan and Mohammad Nazari
Abstract: Applications such as Face Recognition (FR) that deal with high-dimensional data need a mapping technique that introduces representation of low-dimensional features with enhanced discriminatory power and a proper classifier, able to classify those complex features .Most of traditional Linear Discriminant Analysis (LDA) suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the “small sample size” (SSS) problem which is often encountered in FR tasks. In this short paper, we combine nonlinear kernel based mapping of data called KDDA with Support Vector machine (SVM) classifier to deal with both of the shortcomings in an efficient and cost effective manner. The proposed here method is compared, in terms of classification accuracy, to other commonly used FR methods on UMIST face database. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the Eigenfaces, Fisherfaces, and D-LDA methods and traditional linear classifiers.

Area 4 - Motion, Tracking and Stereo Vision

Full Papers
Paper Nr: 43
Title:

A PASSIVE 3D SCANNER - Acquiring High-quality Textured 3D-models Using a Consumer Digital-camera
Authors:

Matthias Elter, Andreas Ernst and Christian Küblbeck
Abstract: We present a low-cost, passive 3d scanning system using an off-the-shelf consumer digital camera for image acquisition. We have developed a state of the art structure from motion algorithm for camera pose estimation and a fast shape from stereo approach for shape reconstruction. We use a volumetric approach to fuse partial shape reconstructions and a texture mapping technique for appearance recovery. We extend the state of the art by applying modifications of standard computer vision techniques to images of very high resolution to generate high quality textured 3d models. Our reconstruction results are robust and visually convincing.
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Paper Nr: 49
Title:

AUTONOMOUS TRACKING SYSTEM FOR AIRPORT LIGHTING QUALITY CONTROL
Authors:

James Niblock, Karen Mcmenemy, S. Ferguson and Jian-xun Peng
Abstract: The central aim of this research is to develop an an autonomous measurement system for assessing the performance of an airport lighting pattern. The system improves safety with regard to aircraft landing procedures by ensuring the airport lighting is properly maintained and conforms to current standards and recommendations laid down by the International Civil Aviation Organisation (ICAO). A vision system, mounted in the cockpit of an aircraft, is capable of capturing sequences of airport lighting images during a normal approach to an aerodrome. These images are post-processeda to determine the grey level of the approach lighting pattern (ALP). In this paper, two tracking algorithms are presented which can detect and track individual luminaires throughout the complete image sequence. The effective tracking of the luminaires is central to the long term goal of this research, which is to assess the performance of the luminaires’ from the recorded grey level data extracted for each detected luminaire. The two algorithms presented are the Niblock-McMenemy (NM) feature tracking algorithm has been optimised for the specific task of airport lighting and to assess its effectiveness it has been compared to the Kanade-Lucus-Tomasi (KLT) feature tracking algorithm. In order to validate both algorithms a synthetic 3D model of the ALP is presented. To further assess the robustness of the algorithms results from an actual approach to a UK aerodromeb are presented. The results show that although both KLT and NM feature trackers are both effective in tracking airport lighting the NM algorithm is better suited to the task due to its reliable grey level information. Limitations, such as the static window size, of the KLT algorithm result in a lossy grey level data and hence lead to inaccurate results.
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Paper Nr: 82
Title:

A REAL-TIME TRACKING SYSTEM COMBINING TEMPLATE-BASED AND FEATURE-BASED APPROACHES
Authors:

Alexander Ladikos, Selim Benhimane and Nassir Navab
Abstract: In this paper we propose a complete real-time model-based tracking system for piecewise-planar objects which combines template-based and feature-based approaches. Our contributions are an extension to the ESM algorithm (Benhimane and Malis, 2004) used for template-based tracking and the formulation of a feature-based tracking approach, which is specifically tailored for use in a real-time setting. In order to cope with highly dynamic scenarios, such as illumination changes, partial occlusions and fast object movement, the system adaptively switches between the template-based tracking, the feature-based tracking and a global initialization phase. Our tracking system achieves real-time performance by applying a coarse-to-fine optimization approach and includes means to detect a loss of track.
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Paper Nr: 100
Title:

RECONSTRUCTING WAFER SURFACES WITH MODEL BASED SHAPE FROM SHADING
Authors:

Alexander Nisenboim and Alfred Bruckstein
Abstract: Model based Shape From Shading (SFS) is a promising paradigm introduced by J. Atick for solving such inverse problems when we happen to have some prior information on the depth profiles to be recovered. In the present work we adopt this approach to address the problem of recovering wafer profiles from images taken by a Scanning Electron Microscope (SEM). This problem arises naturally in the microelectronics inspection industry. A low dimensional model based on our prior knowledge of the types of depth profiles of wafer surfaces has been developed and based on it the SFS problem becomes an optimal parameter estimation. Wavelet techniques were then employed to calculate a good initial guess to be used in Levenberg-Marguardt (LM) minimization process that yields the desired profile parametrization. The proposed algorithm has been tested under both Lambertian and SEM imaging models.
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Paper Nr: 121
Title:

STRUCTURAL ICP ALGORITHM FOR POSE ESTIMATION BASED ON LOCAL FEATURES
Authors:

Marco C. Fabila and Gerald Sommer
Abstract: In this paper we present a new variant of the ICP (iterative closest point) algorithm for finding correspondences between image and model points. This new variant uses structural information from the model points and contour segments detected in images to find better conditioned correspondence sets and to use them to compute the 3D pose. A local representation of 3D free-form contours is used to get the structural information in 3D space and in the image plane. Furthermore, the local structure of free-form contours is combined with orientation and phase as local features obtained from the monogenic signal. With this combination, we achieve a more robust correspondence search. Our approach was tested on synthetical and real data to compare the convergence and performance of our approach against the classical ICP approach.
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Paper Nr: 128
Title:

DISPARITY CONTOUR GROUPING FOR MULTI-OBJECT SEGMENTATION IN DYNAMICALLY TEXTURED SCENES
Authors:

Wei Sun and Stephen Spackman
Abstract: A fast multi-object segmentation algorithm based on disparity contour grouping is described. It segments multiple objects at a wide range of depths from backgrounds of known geometry in a manner insensitive to changing lighting and the dynamic texture of, for example, display surfaces. Not relying on stereo reconstruction or prior knowledge of foreground objects, it is fast enough on commodity hardware for some real-time applications. Experimental results demonstrate its ability to extract object contour from a complex scene and distinguish multiple objects even when they are close together or partially occluded.
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Paper Nr: 156
Title:

USING PHOTOMETRIC STEREO TO REFINE THE GEOMETRY OF A 3D SURFACE MODEL
Authors:

Zsolt Jankó
Abstract: In this paper we aim at refining the geometry of 3D models of real objects by adding surface bumpiness to them. 3D scanners are usually not accurate enough to measure fine details, such as surface roughness. Photometric stereo is an appropriate technique to recover bumpiness. We use a number of images taken from the same viewpoint under varying illumination and an initial sparse 3D mesh obtained by a 3D scanner. We assume the surface to be Lambertian, but the lighting properties are unknown. The novelty of our method is that the initial sparse 3D mesh is exploited to calibrate light sources and then to recover surface normals. The importance of refining the geometry of a bumpy surface is demonstrated by applying the method to synthetic and real data.
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Paper Nr: 172
Title:

COMBINATION OF VIDEO-BASED CAMERA TRACKERS USING A DYNAMICALLY ADAPTED PARTICLE FILTER
Authors:

David Marimon and Touradj Ebrahimi
Abstract: This paper presents a video-based camera tracker that combines marker-based and feature point-based cues in a particle filter framework. The framework relies on their complementary performance. Marker-based trackers can robustly recover camera position and orientation when a reference (marker) is available, but fail once the reference becomes unavailable. On the other hand, feature point tracking can still provide estimates given a limited number of feature points. However, these tend to drift and usually fail to recover when the reference reappears. Therefore, we propose a combination where the estimate of the filter is updated from the individual measurements of each cue. More precisely, the marker-based cue is selected when the marker is available whereas the feature point-based cue is selected otherwise. The feature points tracked are the corners of the marker. Evaluations on real cases show that the fusion of these two approaches outperforms the individual tracking results. Filtering techniques often suffer from the difficulty of modeling the motion with precision. A second related topic presented is an adaptation method for the particle filer. It achieves tolerance to fast motion manoeuvres.
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Paper Nr: 175
Title:

ENERGY MINIMIZATION APPROACH FOR ONLINE DATA ASSOCIATION WITH MISSING DATA
Authors:

Abir El Abed, Séverine Dubuisson and Dominique Béréziat
Abstract: Data association problem is of crucial importance to improve online target tracking performance in many difficult visual environments. Usually, association effectiveness is based on prior information and observation category. However, some problems can arise when targets are quite similar. Therefore, neither the color nor the shape could be helpful informations to achieve the task of data association. Likewise, problems can also arise when tracking deformable targets, under the constraint of missing data, with complex motions. Such restriction, i.e. the lack in prior information, limit the association performance. To remedy, we propose a novel method for data association, inspired from the evolution of the target dynamic model, and based on a global minimization of an energy vector. The main idea is to measure the absolute geometric accuracy between features. Its parameterless constitutes the main advantage of our energy minimization approach. Only one information, the position, is used as input to our algorithm. We have tested our approach on several sequences to show its effectiveness.
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Paper Nr: 191
Title:

MODEL-BASED SHAPE FROM SILHOUETTE - A Solution Involving a Small Number of Views
Authors:

Jean-françois Menudet, Jean-marie Becker, Thierry Fournel and Catherine Mennessier
Abstract: This article presents a model-based approach to Shape From Silhouette reconstruction. It is formulated as a problem of 3D-2D non-rigid registration: a surface model is deformed until it correctly matches the detected silhouettes in the images. An efficient and reliable solution is proposed, based on a Radial Basis Function deformation driven by control points located on the contour generators of the 3D model. Unlike previous methods relying on non-linear optimization techniques, the proposed method only requires a linear system solving. Another advantage of this model-based approach is to produce a surface representation of the visual hull. Moreover, the
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Paper Nr: 219
Title:

GENERATING OPTIMIZED MARKER-BASED RIGID BODIES FOR OPTICAL TRACKING SYSTEMS
Authors:

Frank Steinicke, Christian Jansen, Klaus Hinrichs, Jan Vahrenhold and Bernd Schwald
Abstract: Marker-based optical tracking systems are often used to track objects that are equipped with a certain number of passive or active point markers. Fixed configurations of these markers, so-called rigid bodies, can be detected by, for example, infrared stereo-based camera systems, and their position and orientation can be reconstructed by corresponding tracking algorithms. The main issue in designing the geometrical constellation of these markers and their 3D positions is to allow robust identification and tracking of multiple objects, and this design process is considered to be an essential and challenging task. At present, the design process is based on trial-and-error: the designer constructs a marker configuration, evaluates it in a given setup, and rearranges the marker positions within the configuration if necessary. Even though single ready-made rigid bodies permit sufficiently good tracking, it is not ensured that the corresponding arrangements of markers meet any quality criteria in terms of reliability and robustness. Furthermore, it is unclear whether it is possible to add further rigid bodies to the setup which are sufficiently distinguishable from the given ones. In this paper, we present an approach to semi-automatically generate marker-based rigid bodies which are optimal with respect to the properties of the tracking system for which they are used, e.g., granularity, accuracy, or jitter. Our procedure which is aimed at supporting the design process as well as improving tracking generates configurations for several devices associated with an arbitrary set of point-based markers. We discuss both the technical background of our approach and the results of an evaluation comparing the tracking quality of commercially available devices to the rigid bodies generated by our approach.
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Paper Nr: 277
Title:

FACE TRACKING USING CANONICAL CORRELATION ANALYSIS
Authors:

José Y. Zepeda, Franck Davoine and Maurice Charbit
Abstract: This paper presents an approach that incorporates canonical correlation analysis for monocular 3D face tracking as a rigid object. It also provides the comparison between the linear and the non linear version (kernel) of the CCA. The 3D pose of the face is estimated from observed raw brightness shape-free 2D image patches. A parameterized geometric face model is adopted to crop out and to normalize the shape of patches of interest from video frames. Starting from a face model fitted to an observed human face, the relation between a set of perturbed pose parameters of the face model and the associated image patches is learned using CCA or KCCA. This knowledge is then used to estimate the correction to be added to the pose of the face from an observed patch in the current frame. Experimental results on tracking faces in long video sequences show the effectiveness of the two proposed methods.
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Paper Nr: 295
Title:

A SPATIAL SAMPLING MECHANISM FOR EFFECTIVE BACKGROUND SUBTRACTION
Authors:

Marco Cristani and Vittorio Murino
Abstract: In the video surveillance literature, background (BG) subtraction is an important and fundamental issue. In this context, a consistent group of methods operates at region level, evaluating in fixed zones of interest pixel values’ statistics, so that a per-pixel foreground (FG) labeling can be performed. In this paper, we propose a novel hybrid, pixel/region, approach for background subtraction. The method, named Spatial-Time Adaptive Per Pixel Mixture Of Gaussian (S-TAPPMOG), evaluates pixel statistics considering zones of interest that change continuously over time, adopting a sampling mechanism. In this way, numerous classical BG issues can be efficiently faced: actually, it is possible to model the background information more accurately in the chromatic uniform regions exhibiting stable behavior, thus minimizing foreground camouflages. At the same time, it is possible to model successfully regions of similar color but corrupted by heavy noise, in order to minimize false FG detections. Such approach, outperforming state of the art methods, is able to run in quasi-real time and it can be used at a basis for more structured background subtraction algorithms.
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Short Papers
Paper Nr: 113
Title:

FUSION OF GPS AND VISUAL MOTION ESTIMATES FOR ROBUST OUTDOOR OPEN FIELD LOCALIZATION
Authors:

Hans Jørgen Andersen, Morten F. Christensen and Thomas Bak
Abstract: Localization is an essential part of autonomous vehicles or robots navigating in an outdoor environment. In the absence of an ideal sensor for localization, it is necessary to use sensors in combination in order to achieve acceptable results. In the present study we present a combination of GPS and visual motion estimation, which have complementary strengths. The visual motion estimation is based on the tracking of points in an image sequence. In an open field outdoor environment the points being tracked are typically distributed in one dimension (on a line), which allows the ego motion to be determined by a new method based on simple analysis of the image point set covariance structure. Visual motion estimates are fused with GPS data in a Kalman filter. Since the filter tracks the state estimate over time, it is possible to use the prior estimate of the state to remove errors in the landmark matching, simplifying the matching, and increasing the robustness. The proposed algorithm is evaluated against ground truth in a realistic outdoor experimental setup.
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Paper Nr: 122
Title:

GENERIC OBJECT TRACKING FOR FAST VIDEO ANNOTATION
Authors:

Remi Trichet and Bernard Merialdo
Abstract: This article describes a method for fast video annotation using an object tracking technique. This work is part of the development of a system for interactive television, where video objects have to be identified in the video program. This environment puts specific requirements on the object tracking technique. We propose to use a generic technique based on keypoints. We describe three contributions in order to best satisfy those requirements: a model for a broader temporal use of the keypoints, an ambient color adaptation pre-treatment enhancing the keypoint detector performance, and a motion based bounding box repositioning algorithm. Finally, we present experimental results to validate those contributions.
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Paper Nr: 160
Title:

BACKGROUND SUBTRACTION FOR REALTIME TRACKING OF A TENNIS BALL
Authors:

Jinzi Mao, David Mould and Sriram Subramanian
Abstract: In this paper we investigate real-time tracking of a tennis-ball using various image differencing techniques. First, we considered a simple background subtraction method with subsequent ball verification (BS). We then implemented two variants of our initial background subtraction method. The first is an image differencing technique that considers the difference in ball position between the current and previous frames along with a background model that uses a single Gaussian distribution for each pixel. The second is uses a mixture of Gaussians to accurately model the background image. Each of these three techniques constitutes a complete solution to the tennis ball tracking problem. In a detailed evaluation of the techniques in different lighting conditions we found that the mixture of Gaussians model produces the best quality tracking. Our contribution in this paper is the observation that simple background subtraction can outperform more sophisticated techniques on difficult problems, and we provide a detailed evaluation and comparison of the performance of our techniques, including a breakdown of the sources of error.
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Paper Nr: 205
Title:

RELIABLE DETECTION OF CAMERA MOTION BASED ON WEIGHTED OPTICAL FLOW FITTING
Authors:

Rodrigo Minetto, Neucimar Leite and Jorge Stolfi
Abstract: Our goal in this paper is the reliable detection of camera motion (pan/zoom/tilt) in video records. We propose an algorithm based on weighted optical flow least-square fitting, where an iterative procedure is used to improve the corresponding weights. To the optical flow computation we used the Kanade-Lucas-Tomasi feature tracker. Besides detecting camera motion, our algorithm provides a precise and reliable quantitative analysis of the movements. It also provides a rough segmentation of each frame into “foreground” and “background” regions, corresponding to the moving and stationary parts of the scene, respectively. Tests with two real videos show that the algorithm is fast and efficient, even in the presence of large objects movements.
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Paper Nr: 206
Title:

OCCLUSIONS AND ACTIVE APPEARANCE MODELS
Authors:

McElory Hoffmann, Ben Herbst and Karin Hunter
Abstract: The deterministic active appearance model (AAM) tracker fails to track objects under occlusion. In this paper, we discuss two approaches to improve this tracker ’s robustness and tracking results. The first approach initialises the AAM tracker with a shape estimate obtained from an active contour, incorporating shape history into the tracker. The second approach combines AAMs and the particle filter, consequently employing both shape and texture history into the tracker. For each approach, a simple occlusion detection method is suggested, enabling us to address occlusion.
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Paper Nr: 240
Title:

DETECTING COPLANAR FEATURE POINTS IN HANDHELD IMAGE SEQUENCES
Authors:

Olaf Kähler and Joachim Denzler
Abstract: 3D reconstruction applications can benefit greatly from knowledge about coplanar feature points. Extracting this knowledge from images alone is a difficult task, however. The typical approach to this problem is to search for homographies in a set of point correspondences using the RANSAC algorithm. In this work we focus on two open issues with a blind random search. First, we enforce the detected planes to represent physically present scene planes. Second, we propose methods to identify cases, in which a homography does not imply coplanarity of feature points. Experiments are performed to show applicability of the presented plane detection algorithms to handheld image sequences.
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Paper Nr: 246
Title:

HIERARCHICAL MULTI-RESOLUTION MODEL - For Fast Energy Minimization of Virtual Cloth
Authors:

Le T. Tung and André Gagalowicz
Abstract: In this paper we present a method for fast energy minimization of virtual garments. Our method is based upon the idea of multi-resolution particle system. When garments are approximately positioned around a virtual character, their spring energy may be high, which will cause instability or at least long execution time of the simulation. An energy minimization algorithm is needed; if a fixed resolution is used, it will require many iterations to reduce its energy. Even though the complexity of each iteration is O(n), with a high resolution mass-spring system, this minimization process can take a whole day. The hierarchical method presented in this paper is used to reduce significantly the execution time of the minimization process. The garments are firstly discretized in several resolutions. Once the lowest resolution particles system is minimized (in a short time), a higher resolution model is derived, then minimized. The procedure is iterated up to the highest resolution. But at this stage, the energy to minimize is already much lower so that minimization takes a reasonable time.
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Paper Nr: 274
Title:

3D HUMAN TRACKING WITH GAUSSIAN PROCESS ANNEALED PARTICLE FILTER
Authors:

Leonid Raskin, Ehud Rivlin and Michael Rudzsky
Abstract: We present an approach for tracking human body parts with prelearned motion models in 3D using multiple cameras. We use an annealed particle filter to track the body parts and a Gaussian Process Dynamical Model in order to reduce the dimensionality of the problem, increase the tracker's stability and learn the motion models. We also present an improvement for the weighting function that helps to its use in occluded scenes. We compare our results to the results achieved by a regular annealed particle filter based tracker and show that our algorithm can track well even for low frame rate sequences.
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Paper Nr: 287
Title:

SMARTCAM FOR REAL-TIME STEREO VISION - Address-event Based Embedded System
Authors:

Stephan Schraml, Peter Schön and Nenad Milosevic
Abstract: We present a novel real-time stereo smart camera for sparse disparity (depth) map estimation of moving objects at up to 200 frames/sec. It is based on a 128x128 pixel asynchronous optical transient sensor, using address-event representation (AER) protocol. An address-event based algorithm for stereo depth calculation including calibration, correspondence and reconstruction processing steps is also presented. Due to the on-chip data pre-processing the algorithm can be implemented on a single low-power digital signal processor.
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Paper Nr: 299
Title:

AUTOMATIC KERNEL WIDTH SELECTION FOR NEURAL NETWORK BASED VIDEO OBJECT SEGMENTATION
Authors:

Dubravko Culibrk, Daniel Socek, Oge Marques and Borko Furht
Abstract: Background modelling Neural Networks (BNNs) represent an approach to motion based object segmentation in video sequences. BNNs are probabilistic classifiers with nonparametric, kernel-based estimation of the underlying probability density functions. The paper presents an enhancement of the methodology, introducing automatic estimation and adaptation of the kernel width. The proposed enhancement eliminates the need to determine kernel width empirically. The selection of a kernel-width appropriate for the features used for segmentation is critical to achieving good segmentation results. The improvement makes the methodology easier to use and more adaptive, and facilitates the evaluation of the approach.
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Paper Nr: 309
Title:

PLANNING OF A MULTI STEREO VISUAL SENSOR SYSTEM FOR A HUMAN ACTIVITIES SPACE
Authors:

Jiandan Chen, Siamak Khatibi and Wlodek Kulesza
Abstract: The paper presents a method for planning the position of multiple stereo sensors in an indoor environment. This is a component of an Intelligent Vision Agent System. We propose a new approach to optimize the multiple stereo visual sensor configurations in 3D space in order to get efficient visibility for surveillance, tracking and 3D reconstruction. The paper introduces a constraints method for modelling a Field of View in spherical coordinates, a tetrahedron model for target objects, and a stereo view constraint for the baseline of paired cameras. The constraints were analyzed and the minimum amount of stereo pairs necessary to cover the entire target space was optimized by an integer linear programming. The 3D simulations for human body and activities space coverage in Matlab illustrate the problem.
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Paper Nr: 328
Title:

STREAMING CLUSTERING ALGORITHMS FOR FOREGROUND DETECTION IN COLOR VIDEOS
Authors:

Zoran Duric, Wallace E. Lawson and Dana Richards
Abstract: A new method is given for locating foreground objects in color videos.This is an essential task in many applications such as surveillance. The algorithm uses clustering techniques to permit flexibility and adaptability in the description of the background. The approach is an example of the streaming data paradigm of algorithms design, which only permits limited information to be retained about previous video frames. Experimental results show that it is an effective and robust technique.
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Paper Nr: 357
Title:

DETECTION AND TRACKING OF MULTIPLE MOVING OBJECTS IN VIDEO
Authors:

Wei Huang and Jonathan Wu
Abstract: This paper presents a method for detecting and tracking multiple moving objects in both outdoor and indoor environments. The proposed method measures the change of a combined color-texture feature vector in each image block to detect moving objects. The texture feature is extracted from DCT frequency domain. An attributed relational graph (ARG) is used to represent each object, in which vertices are associated to an object’s sub-regions and edges represent spatial relations among the sub-regions. Object tracking and identification are accomplished by matching the input graph to the model graph. The notion of inexact graph matching enables us to track partially occluded objects. The experimental results prove the efficiency of the proposed method.
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Paper Nr: 362
Title:

HYBRID DYNAMIC SENSORS CALIBRATION FROM CAMERA-TO-CAMERA MAPPING : AN AUTOMATIC APPROACH
Authors:

J. Badri, C. Tilmant, J.-M. Lavest, Quoc-Cuong PHAM and Patrick Sayd
Abstract: Video surveillance becomes more and more extended in industry and often involves automatic calibration system to remain efficient. In this paper, a video-surveillance system is presented that uses stationary-dynamic camera devices. The static camera is used to monitor a global scene. When it detects a moving object, the dynamic camera is controlled to be centered on this object. We describe a method of camera-to-camera calibration in order to command the dynamic camera. This method allows to take into account the intrinsic camera parameters, the 3D scene geometry and the fact that the mechanism of inexpensive dynamic camera does not fit the classical geometrical model. Finally, some experimental results attest the accuracy of the proposed solution.
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Paper Nr: 32
Title:

CAMERA BASED HEAD-MOUSE - Optimization of Template-Based Cross-Correlation Matching
Authors:

Tatiana V. Evreinova, Grigori Evreinov and Roope Raisamo
Abstract: There is a challenge to employ video-based input in mobile applications for access control, games and entertainment computing. However, by virtue of computational complexity, most of algorithms have a low performance and high CPU usage. This paper presents the experimental results of testing the reduced spiral search with the sparse angular sampling and adaptive search radius. We demonstrated that a reliable tracking could be provided in a wide range of lighting conditions with the relative brightness of only 16 pixels composing the grid-like template (the grid step of 10-15 pixels). Cross-correlation matching of the template was implemented in eight directions with a shift of one pixel and adaptive search radius. The algorithm was thoroughly tested and after that used in a text entry application. The mean typing speed achieved with the head tracker and on-screen keyboard was of about 6.2 wpm without prediction after 2 hours practice.
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Paper Nr: 66
Title:

REAL-TIME TEMPLATE BASED TRACKING WITH GLOBAL MOTION COMPENSATION IN UAV VIDEO
Authors:

Yuriy Luzanov, Todd Howlett and Mark Robertson
Abstract: In this paper we describe a combination of Kalman filter with global motion estimation, between consecutive frames, implemented to improve target tracking in the presence of rapid motions of the camera encountered in human operated UAV based video surveillance systems. The global motion estimation allows to retain the localization of the tracked targets provided by the Kalman filter. The original target template is selected by the operator. SSD error measure is used to find the best match for the template in video frames.
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Paper Nr: 66
Title:

REAL-TIME TEMPLATE BASED TRACKING WITH GLOBAL MOTION COMPENSATION IN UAV VIDEO
Authors:

Yuriy Luzanov, Todd Howlett and Mark Robertson
Abstract: In this paper we describe a combination of Kalman filter with global motion estimation, between consecutive frames, implemented to improve target tracking in the presence of rapid motions of the camera encountered in human operated UAV based video surveillance systems. The global motion estimation allows to retain the localization of the tracked targets provided by the Kalman filter. The original target template is selected by the operator. SSD error measure is used to find the best match for the template in video frames.
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Paper Nr: 158
Title:

AUTOMATIC AUGMENTED VIDEO CREATION FOR MARKERLESS ENVIRONMENTS
Authors:

Jairo Roberto Sanchez and Diego Borro
Abstract: In this paper we present an algorithm to calculate the camera motion in a video sequence. Our method can search and track feature points along the video sequence, calibrate pinhole cameras and estimate the camera motion. In the first step, a 2D feature tracker finds and tracks points in the video. Using this information, outliers are detected using epipolar geometry robust estimation techniques. Finally, the geometry is refined using non linear optimization methods obtaining the camera’s intrinsic and extrinsic parameters. Our approach does not need to use markers and there are no geometrical constraints in the scene either. Thanks to the calculated camera pose it is possible to add virtual objects in the video in a realistic manner.
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Paper Nr: 201
Title:

FOOTBALL PLAYER TRACKING FROM MULTIPLE VIEWS - Using a Novel Background Segmentation Algorithm and Multiple Hypothesis Tracking
Authors:

Alexandra Koutsia, Nikos Grammalidis, Kosmas Dimitropoulos, Mustafa Karaman and Lutz Goldmann
Abstract: In this work, our aim is to develop an automated system which provides data useful for football game analysis. Information from multiple cameras is used to perform player detection, classification and tracking. A background segmentation approach, which operates with the invariant Gaussian colour model and uses temporal information, is used to achieve more accurate results. Information derived and matched from all cameras is then used to perform tracking, using an advanced Multiple Hypothesis Tracking algorithm.
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Paper Nr: 201
Title:

FOOTBALL PLAYER TRACKING FROM MULTIPLE VIEWS - Using a Novel Background Segmentation Algorithm and Multiple Hypothesis Tracking
Authors:

Alexandra Koutsia, Nikos Grammalidis, Kosmas Dimitropoulos, Mustafa Karaman and Lutz Goldmann
Abstract: In this work, our aim is to develop an automated system which provides data useful for football game analysis. Information from multiple cameras is used to perform player detection, classification and tracking. A background segmentation approach, which operates with the invariant Gaussian colour model and uses temporal information, is used to achieve more accurate results. Information derived and matched from all cameras is then used to perform tracking, using an advanced Multiple Hypothesis Tracking algorithm.
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Paper Nr: 211
Title:

REAL TIME SMART SURVEILLANCE USING MOTION ANALYSIS
Authors:

Marco Leo, Paolo Spagnolo, Tiziana D'Orazio, P. L. Mazzeo and A. Distante
Abstract: Smart Surveillance is the use of automatic video analysis technologies for surveillance purposes and it is currently one of the most active research topics in computer vision because of the wide spectrum of promising applications. In general, the processing framework for smart surveillance consists of a preliminary and fundamental motion detection step in combination with higher level algorithms that are able to properly manage motion information. In this paper a reliable motion analysis approach is coupled with homographic transformations and a contour comparison procedure to achieve the automatic real-time monitoring of forbidden areas and the detection of abandoned or removed objects. Experimental tests were performed on real image sequences acquired from the Messapic museum of Egnathia (south of Italy).
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Paper Nr: 245
Title:

DIFFERENNTIAL TECHNIQUE FOR MOTION COMPUTATION USING COLOUR INFORMATION
Authors:

Bouden Toufik and Doghmanne Noureddine
Abstract: Optical flow computation is an important and challenging problem in the motion analysis of images sequence. It is a difficult and computationally expensive task and is an ill-posed problem, which expresses itself as the aperture problem. However, optical flow vectors or motion can be estimated by differential techniques using regularization methods; in which additional constraints functions are introduced. In this work we propose to improve differential methods for optical flow estimation by including colour information as constraints functions in the optimization process using a simple matrix inversion. The proposed technique has shown encouraging results.
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Paper Nr: 249
Title:

A STEREOPHOTOGRAMMIC SYSTEM TO POSITION PATIENTS FOR PROTON THERAPY
Authors:

Neil Muller, Evan De Kock, Ruby Van Rooyen and Chris Trauernicht
Abstract: Proton therapy is a successful treatment for lesions that are hard to treat using conventional radiotherapy, as the radiation dose to nearby critical structures can be tightly controlled. To realise these advantages, the patient needs to be accurately positioned, and monitored during treatment to ensure that no motion occurs. iThemba LABS uses a fixed beam-line, and moves the patient using a suitable positioning device. In this paper, we discuss several aspects the stereo vision based system used to both determine the position of the patient in the room, and to monitor the patient during treatment.
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Paper Nr: 269
Title:

ESTIMATING LARGE LOCAL MOTION IN LIVE-CELL IMAGING USING VARIATIONAL OPTICAL FLOW - Towards Motion Tracking in Live Cell Imaging Using Optical Flow
Authors:

Jan Hubený, Vladimír Ulman and Pavel Matula
Abstract: The paper studies the application of state-of-the-art variational optical flow methods for motion tracking of fluorescently labeled targets in living cells. Four variants of variational optical flow methods suitable for this task are briefly described and evaluated in terms of the average angular error. Artificial ground-truth image sequences were generated for the purpose of this evaluation. The aim was to compare the ability of those methods to estimate local divergent motion and their suitability for data with combined global and local motion. Parametric studies were performed in order to find the most suitable parameter adjustment. It is shown that a selected optimally tuned method tested on real 3D input data produced satisfactory results. Finally, it is shown that by using appropriate numerical solution, reasonable computational times can be achieved even for 3D image sequences.
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Paper Nr: 322
Title:

A DISTRIBUTED VISION SYSTEM FOR BOAT TRAFFIC MONITORING IN THE VENICE GRAND CANAL
Authors:

D. Bloisi, Luca Iocchi, Riccardo Leone, R. Pigliacampo, Luigi Tombolini and Luca Novelli
Abstract: In this paper we describe a system for boat traffic monitoring that has been realized for analyzing and computing statistics of trafic in the Grand Canal in Venice. The system is based on a set of survey cells to monitor about 6 Km of canal. Each survey cell contains three cameras oriented in three directions and covering about 250-300 meters of the canal. This paper presents the segmentation and tracking phases that are used to detect and track boats in the channel and experimental evaluation of the system showing the effectiveness of the approach in the required tasks.
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Paper Nr: 204
Title:

REMOVAL OF UNWANTED HAND GESTURES USING MOTION ANALYSIS
Authors:

Khurram Khurshid
Abstract: This work presents an effective method for hand gesture recognition in non-static background conditions and then removal of certain unwanted gestures from the video. For this purpose, we have developed a new approach which mainly focuses on motion analysis of the hand. It requires good detection and tracking of the hand, so innovations have been made in the existing methods. The local and the global motion of the detected hand region is then analyzed using optical flow. The system is initially trained for a gesture and the motion pattern of the hand for that gesture is identified. This pattern is associated with this gesture and is searched for in the test videos. The system thoroughly trained and tested on 20 videos filmed on 4 different people reported a success rate of 90%.