NDLI: Shape prior based foreground segmentation with local rotation and structural changes

Content Provider	IEEE Xplore Digital Library
Author	Xiaojun Wu Jia Chen Wang, M.Y. Xiongwei Wang
Copyright Year	2011
Description	Author affiliation: School of Mechanical Engineering and Automation, Shenzhen Graduate School, HIT, 518055, China (Jia Chen; Wang, M.Y.) \|\| Department of Mechanical and Automation Engineering, The Chinese University of Hongkong, China (Xiongwei Wang) \|\| Faculty in School of Mechanical Engineering and Automation, Shenzhen Graduate School, HIT, 518055, China (Xiaojun Wu)
Abstract	Shape prior based segmentation method has the advantage of robustly tracking moving object. But, it can not deal with local rotations or changes of object structure. In this paper, we propose an algorithm that can accurately segment the moving foreground object with local rotation and object structure change from the low resolution gray scale images based on Schoenemann's method. Two new terms, turning angle and a distance function, are added to the energy function. When estimating the similarity between the prior shape template and the object contour, turning angle is used to measure the curve match, which is rotational invariance and translational invariance. To deal with the object structure change, a distance function is added to the energy function. The experimental results show that the improved new energy function can robustly segment and track the foreground object with local rotations and structure change.
Starting Page	1305
Ending Page	1310
File Size	341479
Page Count	6
File Format	PDF
ISBN	9781457714757
ISSN	19483449
e-ISBN	9781457714764
DOI	10.1109/ICCA.2011.6138005
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-19
Publisher Place	Chile
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape Turning Image edge detection Image segmentation Solid modeling Robustness Educational institutions
Content Type	Text
Resource Type	Article

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