Robust intelligence rests on the ability to reason about missing, incomplete, ambiguous and corrupted data. This is particularly true in visual perception, where an intelligent system is faced with reasoning about the complexity of a changing three-dimensional world given only two-dimensional images. Bayesian inference has become popular for dealing with such problems because it provides a sound way of combining ambiguous sensor measurements with prior knowledge about the world. Priors represent the collected experience of a perceptual system and by integrating heterogeneous sources of information in a statistically sound way enable such a system to respond robustly to novel situations.
Markov random fields (MRFs) provide a powerful and popular formalism for representing visual priors. However, they have typically modeled only local, pairwise pixel interactions, which limit their modeling capabilities. This project aims at increasing the power and applicability of these models using larger pixel neighborhoods (cliques). The proposed Fields-of-Experts (FoE) model generalizes many previous MRF models, and all its parameters can be learned from real-world training data. Preliminary experiments have shown that, for example, image reconstruction applications benefit from such richer visual priors, but many other application domains have remained unexplored. The development of these statistical modeling tools will also have an impact on other domains outside of machine vision where the need for modeling complex, high-dimensional data arises. Finally, the dissemination of the collected experimental data, learned models, and software promises to stimulate research and make possible quantitative comparisons towards better statistical models of the visual world.
