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| Occlusion Boundary Detection Using Graph-based Semi-supervised Learning |
| ZHANG Shi-hui1,2,ZHANG Yu-cheng1,ZHANG Hong-qiao1,LI Xin1 |
1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;
2. The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, Hebei 066004, China |
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Abstract A novel occlusion boundary detection approach is proposed for depth image by using graph-based semi-supervised learning. Firstly, the connected undirected graph is constructed with the labeled and unlabeled pixels as vertexes. Secondly, the feature vector of each pixel is gained by extracting its maximal depth difference and the sum of eight neighborhood effective depth differences, and the similarity between the pixels are computed as the weight of the corresponding edge in the undirected graph. Thirdly, the pixels to be detected in the undirected graph are labeled as occlusion or nonocclusion boundary point according to graph-based semi-supervised learning idea. Finally, the occlusion boundary points in depth image are visualized and the occlusion boundary can be obtained. The experimental results show that, although only a small amount of labeled samples, the proposed approach is equivalent to the existing supervised-based learning method in accuracy.
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Received: 28 November 2014
Published: 14 October 2016
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Corresponding Authors:
Shi-hui ZHANG
E-mail: sshhzz@ysu.edu.cn
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2016, Vol. 37 
