Multi-Stage Unsupervised Learning for Multi-Body Motion Segmentation(Image Recognition, Computer Vision)
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概要
- 論文の詳細を見る
Many techniques have been proposed for segmenting feature point trajectories tracked through a video sequence into independent motions, but objects in the scene are usually assumed to undergo general 3-D motions. As a result, the segmentation accuracy considerably deteriorates in realistic video sequences in which object motions are nearly degenerate. In this paper, we propose a multi-stage unsupervised learning scheme first assuming degenerate motions and then assuming general 3-D motions and show by simulated and real video experiments that the segmentation accuracy significantly improves without compromising the accuracy for general 3-D motions.
- 社団法人電子情報通信学会の論文
- 2004-07-01
著者
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KANATANI Kenichi
Department of Computer Science, Okayama University
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Sugaya Yasuyuki
Department Of Computer Science And Engineering Toyohashi University Of Technology
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Kanatani Kenichi
Department Of Computer Science Gunma University
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