Occlusion Boundary Detection Method for Depth Image Based on Ensemble Learning
ZHANG Shi-hui1,2,PANG Yun-chong1
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;
2. The Key Lab for Computer Virtual Tech and Sys Integration of Hebei Province, Qinhuangdao, Hebei 066004, China
Abstract The existing occlusion detection method for depth image can not effectively detect the occlusion boundary point with less obvious depth change, this status should be changed. The eight neighborhood total depth difference feature and maximal area feature are proposed firstly, and then the calculation methods for these two new features are defined. On this basis, a new occlusion detection approach based on ensemble learning is proposed, which combines the proposed features and existing occlusion related features to train the decision tree-based AdaBoost classifier to classify the pixel of depth image into occlusion boundary point or non-occlusion boundary point. The experimental results show that, compared with the existing methods, the proposed approach has higher accuracy and better universality.
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2014, Vol. 35 
