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A Self-Occlusion Detection Approach Based on Depth Image Using SVM
Author(s) -
Shihui Zhang,
Jianxin Liu
Publication year - 2012
Publication title -
international journal of advanced robotic systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.394
H-Index - 46
eISSN - 1729-8814
pISSN - 1729-8806
DOI - 10.5772/53823
Subject(s) - occlusion , computer science , support vector machine , artificial intelligence , image (mathematics) , computer vision , pattern recognition (psychology) , medicine , cardiology
This paper describes a novel self‐occlusion detection approach for depth image using SVM. This work is distinguished by three contributions. The first contribution is the introduction of a new self‐occlusion detection idea, which takes the self‐occlusion as a classification problem for the first time, thus the accuracy of the detection result is improved. The second contribution is two new self‐occlusion‐related features, named maximal depth difference and included angle. The third contribution is a specific self-occlusion detection algorithm. Experimental results not only show that the proposed approach is feasible and effective, but also show that our works produce better results than those previously published

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