Motion estimation and segmentation

被引:18
作者
Tian, TY
Shah, M
机构
[1] Computer Vision Laboratory, Computer Science Department, University of Central Florida, Orlando
关键词
optical flow; motion boundaries; line process; Markov random fields; mean field techniques;
D O I
10.1007/BF01246637
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In the general structure-from-motion (SFM) problem involving several moving objects in a scene, the essential first step is to segment moving objects independently. We attempt to deal with the problem of optical flow estimation and motion segmentation over a pair of images. We apply a mean field technique to determine optical flow and motion boundaries and present a deterministic algorithm. Since motion discontinuities represented by line process are embedded in the estimation of the optical flow, our algorithm provides accurate estimates of optical flow especially along motion boundaries and handles occlusion and multiple motions. We show that the proposed algorithm outperforms other well-known algorithms in terms of estimation accuracy and timing.
引用
收藏
页码:32 / 42
页数:11
相关论文
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