Learning Low-level Vision Learning Low-level Vision

We show a learning-based method for low-level vision problems{estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images. We model that world with a Markov network, learning the network parameters from the examples. Bayesian belief propagation allows us to e ciently nd a local maximum of the posterior probability for the scene, given the image. We call this approach VISTA{Vision by Image/Scene TrAining. We apply VISTA to the \super-resolution" problem (estimating high frequency details from a low-resolution image), showing good results. For the motion estimation problem, we show gure/ground discrimination, solution of the aperture problem, and lling-in arising from application of the same probabilistic machinery. To appear in: IEEE International Conference on Computer Vision, Corfu, Greece, 1999. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonpro t educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Information Technology Center America; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Information Technology Center America. All rights reserved. Copyright c Mitsubishi Electric Information Technology Center America, 1999 201 Broadway, Cambridge, Massachusetts 02139 1. First printing, TR99-12, March, 1999 2. revised version, July, 1999. Learning low-level vision William T. Freeman and Egon C. Pasztor MERL, a Mitsubishi Electric Res. Lab. 201 Broadway, Cambridge, MA 02139 freeman, [email protected]