基于深度图像利用BP网络实现遮挡边界检测

doi:10.3969/j.issn.1000-1158.2020.10.05

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Abstract Aiming at the occlusion phenomena existing in visual object, an occlusion boundary detection approach is proposed based on machine learning for depth image. Firstly, a novel occlusion related feature named the longest projected line segment is presented according to the depth and spatial information in depth image. Secondly, a nonlinear normalization method is designed to normalize the occlusion related features. Finally, the problem of occlusion boundary detection is taken as a classification problem, meanwhile, the back propagation(BP) neural network is utilized to detect the occlusion boundary and then the detection result is visualized. Compared with existing methods, the proposed approach is more accurate and the generalization performance is better.

Key words： metrology occlusion boundary depth image BP network longest projected line segment nonlinear normalization

Received: 01 August 2018 Published: 10 October 2020

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TB96

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	Articles by authors
	ZHANG Shi-hui
	GENG Yong
	ZHANG Xiao-wei

Cite this article:

ZHANG Shi-hui,GENG Yong,ZHANG Xiao-wei. Using BP Network for Occlusion Boundary Detection Based on Depth Image[J]. Acta Metrologica Sinica, 2020, 41(10): 1205-1211.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.10.05 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I10/1205

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