Image segmentation by multigrid MRF and perceptual optimization

Jun Zhang; Dongyan Wang; Jianhua Liu

doi:10.1117/12.269774

1 April 1997 Image segmentation by multigrid MRF and perceptual optimization

Jun Zhang, Dongyan Wang, Jianhua Liu

Proceedings Volume 3030, Applications of Artificial Neural Networks in Image Processing II; (1997) https://doi.org/10.1117/12.269774
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

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.

Citation Download Citation

Jun Zhang, Dongyan Wang, and Jianhua Liu "Image segmentation by multigrid MRF and perceptual optimization", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); https://doi.org/10.1117/12.269774

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