Learning low-level vision
|MERL Report: ||TR99-12: William T. Freeman, Egon C. Pasztor
IEEE International Conference on Computer Vision, Corfu, Greece, 1999
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 efficiently find 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 figure/ground discrimination, solution of the aperture problem, and filling-in arising from application of the same probabilistic machinery.