A Probabilistic Generative Approach to Invariant Visual Inference and Learning

Inference and learning from visual data is a challenging task because of noise and the data's ambiguity. The most advanced vision systems to date are the sensory visual circuitries of higher vertebrates. Although artificial approaches make continuous progress, they are for the majority of applications an unequal match to such biological systems so far. To understand and to rebuild biological systems, they have been modeled using approaches from artificial intelligence, artificial neural networks, and probabilistic models. In terms of how the problem of invariant recognition is approached, these models can coarsely be grouped into two classes: models that passively treat invariances (e.g., [1,2]) and models that actively address the typical transformation invariances of object identities (e.g., [3-6]). The former approaches are often feed-forward while the latter approaches are usually recurrent.