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Proceedings Paper

Image segmentation by multigrid MRF and perceptual optimization
Author(s): Jun Zhang; Dongyan Wang; Jianhua Liu
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Paper Abstract

This paper describes a Markov random field (MRF) approach to image segmentation. Unlike most previous MRF techniques, which are based on pixel-classification, this approach groups pixels that are similar. This removes the need to know the number of image classes. Mean field theory and multigrid processing are used in the subsequent optimization to find a good segmentation and to alleviate local minimum problems. Variations of the MRF approach are investigated by incorporating features/schemes motivated by characteristics of the human vision system (HVS). Preliminary results are promising and indicate that multi-grid and HVS based features/schemes can significantly improve segmentation results.

Paper Details

Date Published: 1 April 1997
PDF: 12 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269774
Show Author Affiliations
Jun Zhang, Univ. of Wisconsin/Milwaukee (United States)
Dongyan Wang, Univ. of Wisconsin/Milwaukee (United States)
Jianhua Liu, Univ. of Wisconsin/Milwaukee (United States)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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