TR99-12
Learning low-level vision
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- "Learning low-level vision", Tech. Rep. TR99-12, Mitsubishi Electric Research Laboratories, Cambridge, MA, July 1999.BibTeX TR99-12 PDF
- @techreport{MERL_TR99-12,
- author = {William T. Freeman, Egon C. Pasztor},
- title = {Learning low-level vision},
- institution = {MERL - Mitsubishi Electric Research Laboratories},
- address = {Cambridge, MA 02139},
- number = {TR99-12},
- month = jul,
- year = 1999,
- url = {https://www.merl.com/publications/TR99-12/}
- }
,
- "Learning low-level vision", Tech. Rep. TR99-12, Mitsubishi Electric Research Laboratories, Cambridge, MA, July 1999.
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Research Areas:
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.