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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Subakan, O.N. Vemuri, B.C. |
| Copyright Year | 2008 |
| Description | Author affiliation: Dept. of Comput. & Inf. Sci. & Eng., Florida Univ., Gainesville, FL (Subakan, O.N.; Vemuri, B.C.) |
| Abstract | Image segmentation is a fundamental task in Computer Vision and there are numerous algorithms that have been successfully applied in various domains. There are still plenty of challenges to be met with. In this paper, we consider one such challenge, that of achieving segmentation while preserving complicated and detailed features present in the image, be it a gray level or a textured image. We present a novel approach that does not make use of any prior information about the objects in the image being segmented. Segmentation is achieved using local orientation information, which is obtained via the application of a steerable Gabor filter bank, in a statistical framework. This information is used to construct a spatially varying kernel called the Rigaut Kernel, which is then convolved with the signed distance function of an evolving contour (placed in the image) to achieve segmentation. We present numerous experimental results on real images, including a quantitative evaluation. Superior performance of our technique is depicted via comparison to the state-of-the-art algorithms in literature. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 770115 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424422425 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2008.4587460 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2008-06-23 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Convolution Kernel Gabor filters Active contours Computer vision Context modeling Solid modeling Level set Layout |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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