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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Myronenko, A. Xubo Song |
| Copyright Year | 2009 |
| Description | Author affiliation: Dept. of Sci. & Eng., Oregon Health & Sci. Univ., Beaverton, OR, USA (Myronenko, A.; Xubo Song) |
| Abstract | Active contours is a popular technique for image segmentation. However, active contour tend to converge to the closest local minimum of its energy function and often requires a close boundary initialization. We introduce a new approach that overcomes the close boundary initialization problem by reformulating the external energy term. We treat the active contour as a mean curve of the probability density function p(x). It moves to minimize the Kullback-Leibler (KL) divergence between p(x) and the probability density function derived from the image. KL divergence forces p(x) to “cover all image areas” and the uncovered areas are heavily penalized, which allows the active contour to go over the edges. Also we use deterministic annealing on the width of p(x) to implement a coarse-to-fine search strategy. In the limit, when the width of p(x) goes to zero, the KL divergence function converges to the conventional external energy term (which can be seen a special case) of active contours. Our method produces robust segmentation results from arbitrary initialization positions. |
| Starting Page | 2798 |
| Ending Page | 2804 |
| File Size | 3247144 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424439928 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2009.5206552 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Active contours Probability density function Shape Biomedical imaging Image converters Annealing Robustness Power engineering and energy Deformable models |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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