Scope:
Many problems of Early Vision, Mono or Multimodal image formation, Image recovery and restoration, Segmentation and labelling, Tomographic image reconstruction, 3D from 2D, etc. can be presented as inverse problems and, very often, these inverse problems are ill-posed.

The Bayesian approach has shown his need and efficiency for proposing real solutions to these inverse problems. The main objective of this special session is to regroup new advances and examples of application of this approach to those inverse problems.

Scope:
In real life, visual learning is supposed to be a continuous process. Opposite to this, in machine vision, learning has been so far considered in a limited and isolated way. Most of the nowadays approaches, with very few exceptions, require the intervention of the human operator to collect, segment and store hand-picked data and train pattern classifiers with them. However, for real world scenarios, it is unlikely to know beforehand the number of total classes or the exact number of instances per class.

There is a recent trend in pattern recognition represented by online learning approaches, which treat to continuously update the data representation when new information arrives. Starting with a minimal set, the initial knowledge is expanded, by incorporating incoming instances, some of them which have not been previously available or foreseen at the application creation stage. An interesting characteristic of this strategy is that the train and test phases take place simultaneously. Given the increasing interest on this subject, the current call for papers is intended to offer the possibility to discuss the most recent advances in the area.