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Proceedings Paper

Specularity-invariant crop extraction with probabilistic super-pixel Markov random field
Author(s): Zhenghong Yu; Zhiguo Cao; Mengni Ye; Xiaodong Bai; Yanan Li; Yu Wang
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Paper Abstract

In this paper, we propose a specularity-invariant crop extraction method using probabilistic super-pixel markov random field (MRF). Our method is based on the underlying rule that intensity change gradually between highlight areas and its neighboring non-highlight areas. This prior knowledge is embedded into the MRF-MAP framework by modeling the local and mutual evidences of nodes. The marginal probability of each node in the label field is then iteratively computed by Belief Propagation algorithm which leads to the final solution. Comparing experimental results show that our method outperforms the other commonly used extraction methods in yielding highest performance with the lowest standard deviation.

Paper Details

Date Published: 26 October 2013
PDF: 8 pages
Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 891806 (26 October 2013); doi: 10.1117/12.2031018
Show Author Affiliations
Zhenghong Yu, Huazhong Univ. of Science and Technology (China)
Zhiguo Cao, Huazhong Univ. of Science and Technology (China)
Mengni Ye, Huazhong Univ. of Science and Technology (China)
Xiaodong Bai, Huazhong Univ. of Science and Technology (China)
Yanan Li, Huazhong Univ. of Science and Technology (China)
Yu Wang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 8918:
MIPPR 2013: Automatic Target Recognition and Navigation
Tianxu Zhang; Nong Sang, Editor(s)

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