3D saliency detection based on background detection |
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| Institution: | 1. Institute of Information Science, Beijing Jiaotong University, Beijing Key Laboratory of Advanced Information Science and Network, Beijing 100044, China;2. School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China;1. College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China;2. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan;1. School of Electronic Information Engineering, Tianjin University, Tianjin 300072, China;2. School of Computing, National University of Singapore, Singapore;1. Department of Computer Science, University of California, Irvine, USA;2. School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China;3. Faculty of Information and Communication Technology, Mahidol University, Thailand;1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, Guangdong 510006, China;2. Department of Computer Science, Rutgers University, NJ 08854-8019, USA;3. School of Computer Engineering, Nanyang Technological University, 639798 Singapore, Singapore;4. School of Electronic Engineering, Xidian University, Xi’an, Shaanxi 710071, China;5. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, Guangdong 510642, China |
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| Abstract: | Unlike 2D saliency detection, 3D saliency detection can consider the effects of depth and binocular parallax. In this paper, we propose a 3D saliency detection approach based on background detection via depth information. With the aid of the synergism between a color image and the corresponding depth map, our approach can detect the distant background and surfaces with gradual changes in depth. We then use the detected background to predict the potential characteristics of the background regions that are occluded by foreground objects through polynomial fitting; this step imitates the human imagination/envisioning process. Finally, a saliency map is obtained based on the contrast between the foreground objects and the potential background. We compare our approach with 14 state-of-the-art saliency detection methods on three publicly available databases. The proposed model demonstrates good performance and succeeds in detecting and removing backgrounds and surfaces of gradually varying depth on all tested databases. |
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| Keywords: | 3D saliency detection Background detection Envisioned background Polynomial fitting Salient region |
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