A Comparative Study of Some Markov Random Fields and Different Criteria Optimization in Image Restoration

Rosa, J.I. de la; Villa, Jesús; Araiza, M.A.; González, Efrén; Rosa, Enrique de la

doi:10.5772/37900

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2015

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“…9 In the segmentation problem, the marks of the regions for each pixel appear as x, and the correspondence between the probabilities that these marks take different or identical values depends on the degree of similarity of the pixels with respect to any attributes (for example, brightness or texture attributes). 10 When dense maps of the stereo disparateness are being constructed, the disparateness values of all the pixels appear as x.…”

Section: Graphical Probability Modelsmentioning

confidence: 99%

Generative and probability models in image processing and computer vision

Potapov

2015

J. Opt. Technol.

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This paper gives an analysis of the role of generative models in image processing and computer vision. Oriented and unoriented graphical models (Bayesian and Markov networks) are considered, along with the possibilities of using them in image processing, in particular, to solve problems of noise filtering, segmentation, and stereo vision. Probability programming is considered as a method of specifying arbitrary generative models that possess substantially larger expressive force than graphical models. It is shown that the main limitation of probability programming is associated with the problem of the output efficiency on arbitrary generative models, for the solution of which it is necessary to develop methods of automatic specialization of general output procedures.

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Section: Graphical Probability Modelsmentioning

confidence: 99%

Generative and probability models in image processing and computer vision

Potapov

2015

J. Opt. Technol.

View full text Add to dashboard Cite

show abstract

A Comparative Study of Some Markov Random Fields and Different Criteria Optimization in Image Restoration

Cited by 1 publication

References 56 publications

Generative and probability models in image processing and computer vision

Generative and probability models in image processing and computer vision

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