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On Markov random field models for segmentation of noisy images

  • Published:
Journal of Electronics (China)

An Erratum to this article was published on 01 July 1996

Abstract

Markov random field(MRF) models for segmentation of noisy images are discussed. According to the maximuma posteriori criterion a configuration of an image field is regarded as an optimal estimate of the original scene when its energy is minimized. However, the minimum energy configuration does not correspond to the scene on edges of a given image, which results in errors of segmentation. Improvements of the model are made and a relaxation algorithm based on the improved model is presented using the edge information obtained by a coarse-to-fine procedure. Some examples are presented to illustrate the applicability of the algorithm to segmentation of noisy images.

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Authors and Affiliations

  1. Department of Radio-Electronics, Beijing Normal University, 100875, Beijing

    Kuang Jinyu & Zhu Junxiu

Authors
  1. Kuang Jinyu
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  2. Zhu Junxiu
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Additional information

Supported by the National Natural Science Foundation of China.

An erratum to this article is available at http://dx.doi.org/10.1007/BF02685839.

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Jinyu, K., Junxiu, Z. On Markov random field models for segmentation of noisy images. J. of Electron.(China) 13, 31–39 (1996). https://doi.org/10.1007/BF02684712

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  • DOI: https://doi.org/10.1007/BF02684712

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