Learning low-level vision

William T. Freeman, Egon C. Pasztor, Owen T. Carmichael

Research output: Contribution to journalArticle

1129 Scopus citations

Abstract

We describe 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, modeling their relationships with a Markov network. Bayesian belief propagation allows us to efficiently find a local maximum of the posterior probability for the scene, given an 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. To illustrate the potential breadth of the technique, we also apply it in two other problem domains, both simplified. We learn to distinguish shading from reflectance variations in a single image under particular lighting conditions. For the motion estimation problem in a 'blobs world', we show figure/ground discrimination, solution of the aperture problem, and filling-in arising from application of the same probabilistic machinery.

Original languageEnglish (US)
Pages (from-to)25-47
Number of pages23
JournalInternational Journal of Computer Vision
Volume40
Issue number1
DOIs
StatePublished - Oct 2000
Externally publishedYes

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

Cite this

Freeman, W. T., Pasztor, E. C., & Carmichael, O. T. (2000). Learning low-level vision. International Journal of Computer Vision, 40(1), 25-47. https://doi.org/10.1023/A:1026501619075