Yair Weiss Hebrew University Learning to Perceive from Image Statistics- a Computational Challenge Natural images take up only a tiny fraction of the space of all possible NxN matrices. It thus makes sense that perceptual systems, both biological and artificial, would use image statistics to improve their performance. This idea goes back at least to Mach (1886) and Helmholtz (1925) operationalizing it for computer vision has proven to be difficult. In this talk I will describe the computational challenges raised by such an approach - learning very non Gaussian distributions in high dimensional spaces and performing inference with such distributions. I will then summarize some of our research in this direction. The good news is that very simple statistical models can lead to surprisingly powerful algorithms. The bad news is that even these simple statistical models lead to complicated optimization problems. Specifically, I will discuss applications of image statistics to image denoising, "inpainting" and transparency. Joint work with A. Levin, A. Zomet and E. Levi. Yair Weiss is a senior lecturer at the Hebrew University School of Computer Science and Engineering. He received his Ph.D. from the Massachusetts Institute of Technology and was a visiting scientist at U.C. Berkeley. His research interests include human and machine vision, machine learning and error correcting codes.