Specularity-invariant crop extraction with probabilistic super-pixel Markov random field

Zhenghong Yu; Zhiguo Cao; Mengni Ye; Xiaodong Bai; Yanan Li; Yu Wang

doi:10.1117/12.2031018

26 October 2013 Specularity-invariant crop extraction with probabilistic super-pixel Markov random field

Zhenghong Yu, Zhiguo Cao, Mengni Ye, Xiaodong Bai, Yanan Li, Yu Wang

Proceedings Volume 8918, MIPPR 2013: Automatic Target Recognition and Navigation; 891806 (2013) https://doi.org/10.1117/12.2031018
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China

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.

Citation Download Citation

Zhenghong Yu, Zhiguo Cao, Mengni Ye, Xiaodong Bai, Yanan Li, and Yu Wang "Specularity-invariant crop extraction with probabilistic super-pixel Markov random field", Proc. SPIE 8918, MIPPR 2013: Automatic Target Recognition and Navigation, 891806 (26 October 2013); https://doi.org/10.1117/12.2031018

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