Title:
Localizing Region-Based Active Contours
Localizing Region-Based Active Contours
| dc.contributor.author | Lankton, Shawn | |
| dc.contributor.author | Tannenbaum, Allen R. | |
| dc.contributor.corporatename | Georgia Institute of Technology. Dept. of Biomedical Engineering | |
| dc.contributor.corporatename | Emory University. Dept. of Biomedical Engineering | |
| dc.date.accessioned | 2009-05-18T19:24:54Z | |
| dc.date.available | 2009-05-18T19:24:54Z | |
| dc.date.issued | 2008-11 | |
| dc.description | ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. | en |
| dc.description | DOI: 10.1109/TIP.2008.2004611 | |
| dc.description.abstract | In this paper, we propose a natural framework that allows any region-based segmentation energy to be re-formulated in a local way. We consider local rather than global image statistics and evolve a contour based on local information. Localized contours are capable of segmenting objects with heterogeneous feature profiles that would be difficult to capture correctly using a standard global method. The presented technique is versatile enough to be used with any global region-based active contour energy and instill in it the benefits of localization. We describe this framework and demonstrate the localization of three well-known energies in order to illustrate how our framework can be applied to any energy. We then compare each localized energy to its global counterpart to show the improvements that can be achieved. Next, an in-depth study of the behaviors of these energies in response to the degree of localization is given. Finally, we show results on challenging images to illustrate the robust and accurate segmentations that are possible with this new class of active contour models. | en |
| dc.identifier.citation | Shawn Lankton and Allen Tannenbaum, "Localizing Region-Based Active Contours," IEEE Transactions on Image Processing, Vol. 17, No. 11, November 2008, 2029-2039 | en |
| dc.identifier.issn | 1057-7149 | |
| dc.identifier.uri | http://hdl.handle.net/1853/27928 | |
| dc.language.iso | en_US | en |
| dc.publisher | Georgia Institute of Technology | en |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | |
| dc.subject | Active contours | en |
| dc.subject | Level set methods | en |
| dc.subject | Curve evolution | en |
| dc.subject | Image segmentation | en |
| dc.subject | Partial differential equations | en |
| dc.subject | Multiregion segmentation | en |
| dc.title | Localizing Region-Based Active Contours | en |
| dc.type | Text | |
| dc.type.genre | Article | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | Wallace H. Coulter Department of Biomedical Engineering | |
| relation.isOrgUnitOfPublication | da59be3c-3d0a-41da-91b9-ebe2ecc83b66 |