УДК: 004.932

Generative and probability models in image processing and computer vision

Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Потапов А.С. Генеративные и вероятностные модели в обработке изображений и компьютерном зрении // Оптический журнал. 2015. Т. 82. № 8. С. 5–10.

Potapov A.S. Generative and probability models in image processing and computer vision [in Russian] // Opticheskii Zhurnal. 2015. V. 82. № 8. P. 5–10.

For citation (Journal of Optical Technology):

A. S. Potapov, "Generative and probability models in image processing and computer vision," Journal of Optical Technology. 82(8), 495-498 (2015). https://doi.org/10.1364/JOT.82.000495

Abstract:

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.

Keywords:

image analysis, generative models, graphical models, Bayesian networks, Markov networks, probability programming

Acknowledgements:

This work was carried out with the support of the Ministry of Education and Science of the Russian Federation and with the partial state support of the leading universities of the Russian Federation (Subsidy 074-U01).

OCIS codes: 150.1135

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