Yair Weiss

Learning and Inference in Low-Level Vision

Low level vision addresses the issues of labeling and organizing image pixels according to scene related properties - such as motion, contrast, depth and reflectance. I will describe our attempts to understand low-level vision in humans and machines as optimal inference given the statistics of the world. In particular, I will show how message passing algorithms allow us to solve real-world instances of NP-hard problems and to efficiently learn energy functions despite an exponential number of constraints.