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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Grady, L. |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. of Imaging & Visualization, Siemens Corporate Res., Princeton, NJ, USA (Grady, L.) |
| Abstract | The recently introduced random walker segmentation algorithm by Grady and Funka-Lea (2004) has been shown to have desirable theoretical properties and to perform well on a wide variety of images in practice. However, this algorithm requires user-specified labels and produces a segmentation where each segment is connected to a labeled pixel. We show that incorporation of a nonparametric probability density model allows for an extended random walkers algorithm that can locate disconnected objects and does not require user-specified labels. Finally, we show that this formulation leads to a deep connection with the popular graph cuts method by Boykov et al. (2001) and Wu and Leahy (1993). |
| Sponsorship | IEEE Comput. Soc |
| Starting Page | 763 |
| Ending Page | 770 |
| File Size | 360852 |
| Page Count | 8 |
| File Format | |
| ISBN | 0769523722 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2005.239 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image segmentation Pattern recognition Scattering Computer vision Random variables Visualization Probability Machine learning algorithms Machine learning System testing |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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