期刊
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017)
卷 -, 期 -, 页码 3008-3014出版社
IEEE
DOI: 10.1109/ICCVW.2017.355
关键词
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资金
- National Natural Science Foundation of China [U1611461]
- Science and Technology Planning Project of Guangdong Province, China [2014B090910001]
- grant of Shenzhen Peacock Plan [20130408-183003656]
Saliency detection, finding the most important parts of an image, has become increasingly popular in computer vision. Existing proposal methods are mostly based on color information, which may not be effective for cluttered backgrounds. We propose a new algorithm leveraging stereopsis to generate optical flow which can obtain addition cue (depth cue) to get the final saliency map. The proposed framework consists of three pathways. The first pathway eliminates the background based on cellular automata. The second pathway gets the optical flow and color flow saliency map. The third pathway calculates a coarse saliency map. Finally, we fuse these three pathways to generate the final saliency map. Besides, we construct a new high-quality dataset with the complex scene to make computer challenge human vision. Experimental results on our dataset and another three popular datasets demonstrate that our method is superior to the existing methods in terms of robustness.
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