Timeline

The proceedings are now available online

Program

14:10 Session IV: Statistics of Image Sequences

Goals and Scope

In the last decade, there has been a convergence of statistical and model-based approaches to computational vision. This is an ongoing process, leading to the emerging paradigm of generative-model-based (GMB) vision.

• the formulation of a parameterized probabilistic model of image generation;
• estimation and/or maximization of the posterior probability (given an image or image sequence) of model parameters (state variables).

This workshop aims to bring together researchers working on different problems within computational vision, who are interested in the approach outlined above. To facilitate the exchange of information, it is recommended that papers be organized with separate sections on the generative model and the method of parameter estimation, in addition to Introduction, Results and Conclusions.

In addition to papers describing new GMB algorithms, also appropriate to the workshop are

• papers which focus on a detailed study of generative models (e.g. as models of the statistics of natural images);
• papers which present new estimation methods for model parameters, or compare different estimation methods applied to the same generative model;
• papers providing a GMB interpretation (or modification) of established vision algorithms.

Examples of topics relevant to the workshop include:

• Linear and semi-linear models for the generation of images and image textures
• Independent Component Analysis and extensions, as applied to images
• Applications of the Kalman filter and related methods to vision
• Applications of Gibbs sampling and Condensation to vision
• Statistically-based methods in shape recovery
• Active contours
• Active appearance models
• Models of background images and applications to target detection and tracking