Markov networks for low-level vision
We seek a learning-based algorithm that applies to various low-level vision problems. For each problem, we want to nd the scene interpretation that best explains image data. For example, we may want to infer the projected velocities (scene) which best explain two consecutive image frames (image). From synthetic data, we model the relationship between local image and scene regions, and between a scene region and neighboring scene regions. Three probabilities are learned, which characterize the low-level vision algorithm: the local prior, the local likelihood, and the the conditional probabilities of scene neighbors. Given a new image, we propagate likelihood functions in a Markov network to infer the underlying scene. We use a factorization approximation, ignoring the e ect of loops. This yields an e cient method to infer low-level scene interpretations, which we always nd to be stable. We illustrate the method with di erent representations, and show it working for three applications: an explanatory example, motion analysis and estimating high resolution images from low-resolution ones. 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, TR99-08, February, 1999 Markov networks for low-level vision William T. Freeman and Egon C. Pasztor MERL, a Mitsubishi Electric Res. Lab. 201 Broadway Cambridge, MA 02139 freeman, pasztor@merl.com