MRF model with adaptive multiresolution for image segmentation

Qinling Dai; Chen Zheng; Dingqian Sun; Leiguang Wang

doi:10.1117/12.2019620

4 March 2013 MRF model with adaptive multiresolution for image segmentation

Qinling Dai, Chen Zheng, Dingqian Sun, Leiguang Wang

Proceedings Volume 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering; 876107 (2013) https://doi.org/10.1117/12.2019620
Event: Third International Conference on Photonics and Image in Agriculture Engineering (PIAGENG 2013), 2013, Sanya, China

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.

Citation Download Citation

Qinling Dai, Chen Zheng, Dingqian Sun, and Leiguang Wang "MRF model with adaptive multiresolution for image segmentation", Proc. SPIE 8761, PIAGENG 2013: Image Processing and Photonics for Agricultural Engineering, 876107 (4 March 2013); https://doi.org/10.1117/12.2019620

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