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Low-level computational mono and stereo vision: A Bayesian approach

  • Image Models
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Computer Analysis of Images and Patterns (CAIP 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 719))

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Abstract

Generating models of Markov random fields with Gibbs probability distributions and Bayesian decisions are promising in low-level digital image processing. Some theoretical aspects of the approach are discussed including inherent links with the statistical physics, candidates for the Gibbs models of piecewise-homogeneous images, estimation of parameters, etc.

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Authors and Affiliations

  1. Image Recognition Dept., V. M.Glushkov Institute of Cybernetics, Academy of Sciences of Ukraine, 252650, Kiev-207, Ukraine

    G. L. Gimel'farb

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  1. G. L. Gimel'farb
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Dmitry Chetverikov Walter G. Kropatsch

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© 1993 Springer-Verlag Berlin Heidelberg

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Gimel'farb, G.L. (1993). Low-level computational mono and stereo vision: A Bayesian approach. In: Chetverikov, D., Kropatsch, W.G. (eds) Computer Analysis of Images and Patterns. CAIP 1993. Lecture Notes in Computer Science, vol 719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57233-3_2

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  • DOI: https://doi.org/10.1007/3-540-57233-3_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57233-6

  • Online ISBN: 978-3-540-47980-2

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