Title:
Knowledge-Based Segmentation for Tracking Through Deep Turbulence
Knowledge-Based Segmentation for Tracking Through Deep Turbulence
| dc.contributor.author | Vela, Patricio A. | |
| dc.contributor.author | Niethammer, Marc | |
| dc.contributor.author | Pryor, Gallagher D. | |
| dc.contributor.author | Tannenbaum, Allen R. | |
| dc.contributor.author | Butts, Robert | |
| dc.contributor.author | Washburn, Donald | |
| dc.contributor.corporatename | Air Force Research Laboratory (Kirtland Air Force Base, NM). Directed Energy Directorate | |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Electrical and Computer Engineering | |
| dc.date.accessioned | 2009-07-22T19:18:31Z | |
| dc.date.available | 2009-07-22T19:18:31Z | |
| dc.date.issued | 2008-05 | |
| 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/TCST.2007.899723 | |
| dc.description.abstract | A combined knowledge-based segmentation/active contour algorithm is used for target tracking through turbulence. The algorithm utilizes Bayesian modeling for segmentation of noisy imagery obtained through longrange, laser imaging of a distance target, and active contours for tip tracking. The algorithm demonstrates improved target tracking performance when compared to weighted centroiding. Open-loop and closed-loop comparisons of the algorithms using simulated imagery validate the hypothesis. Index Terms—Active contours, Bayesian statistics, geometric flows, tracking, turbulence. | en |
| dc.identifier.citation | Vela, P.A., Niethammer, M., Pryor, G.D., Tannenbaum, A.R., Butts, R., Washburn, D., "Knowledge-Based Segmentation for Tracking Through Deep Turbulence," IEEE Transactions on Control Systems Technology, Vol. 16, No. 3, May 2008, 469-474 | en |
| dc.identifier.issn | 1063-6536 | |
| dc.identifier.uri | http://hdl.handle.net/1853/29151 | |
| 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 | Bayesian statistics | en |
| dc.subject | Geometric flows | en |
| dc.subject | Tracking | en |
| dc.subject | Turbulence | en |
| dc.title | Knowledge-Based Segmentation for Tracking Through Deep Turbulence | 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 |