Learning Low-Level Vision

  • Authors:
  • William T. Freeman;Egon C. Pasztor

  • Affiliations:
  • -;-

  • Venue:
  • ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
  • Year:
  • 1999

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Abstract

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.