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Proceedings Paper

MRF model with adaptive multiresolution for image segmentation
Author(s): Qinling Dai; Chen Zheng; Dingqian Sun; Leiguang Wang
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Paper Abstract

This paper proposes a Markov random field (MRF) model with adaptive selection multiresolution (MRF-AM) for texture image segmentation. By considering the wavelet decomposition and the morphological wavelet decomposition, MRFAM adaptively selects the multiresolution representation as features from the wavelet and morphological wavelet stepby- step. Then, the MRF is employed to model the features of adaptive multiresolution. The segmentation results are finally obtained by maximizing a posterior probability of the MRF. Experiments demonstrate that our method can improve the segmentation accuracy compared with the deterministic multi-resolution method.

Paper Details

Date Published: 4 March 2013
PDF: 6 pages
Proc. SPIE 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering, 876107 (4 March 2013); doi: 10.1117/12.2019620
Show Author Affiliations
Qinling Dai, Southwest Forestry Univ. (China)
Chen Zheng, Henan Univ. (China)
Dingqian Sun, Southeast Univ. (China)
Leiguang Wang, Southwest Forestry Univ. (China)


Published in SPIE Proceedings Vol. 8761:
PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering
Honghua Tan, Editor(s)

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