Conference Paper

A variational framework for image segmentation combining motion estimation and shape regularization

Dept. of Comput. Sci., Univ. of California, Los Angeles, CA, USA
DOI: 10.1109/CVPR.2003.1211337 Conference: Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, Volume: 1
Source: DBLP

ABSTRACT Based on a geometric interpretation of the optic flow constraint equation, we propose a conditional probability on the spatio-temporal image gradient. We consistently derive a variational approach for the segmentation of the image domain into regions of homogeneous motion. The proposed energy functional extends the Mumford-Shah functional from gray value segmentation to motion segmentation. It depends on the spatio-temporal image gradient calculated from only two consecutive images of an image sequence. Moreover, it depends on motion vectors for a set of regions and a boundary separating these regions. In contrast to most alternative approaches, the problems of motion estimation and motion segmentation are jointly solved by minimizing a single functional. Numerical evaluation with both explicit and implicit (level set based) representations of the boundary shows the strengths and limitations of our approach.

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    • "Other methods use motion projection to link segments, i.e., the position of a segment in a future frame is estimated from its current position and motion features [6] [7] [8] [9]. Cremers (2003) tracked motion segments by jointly solving motion segmentation and motion estimation by minimizing a single energy functional [10]. "
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    • "With the advantage of being implicit, intrinsic and parameter-free, level sets track moving interfaces through either model-free (Paragios and Deriche, 2002) or either model-based (Cremers, 2003) methods. "
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    • "Recently some research works have been done on the motion based image segmentation [4] [6] [12]. These kinds of work do the optical flow estimation and image segmentation simultaneously. "
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