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dc.contributor.authorKarasev, Peter
dc.contributor.authorMalcolm, James G.
dc.contributor.authorTannenbaum, Allen R.
dc.date.accessioned2009-05-27T20:01:33Z
dc.date.available2009-05-27T20:01:33Z
dc.date.issued2008-10
dc.identifier.citationPeter Karasev, James Malcolm, Allen Tannenbaum, "Kernel-based high-dimensional histogram estimation for visual tracking," International Conference on Image Processing, 2008, 2728-2731en
dc.identifier.isbn978-1-4244-1765-0
dc.identifier.issn1522-4880
dc.identifier.urihttp://hdl.handle.net/1853/27945
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.descriptionPresented at the 15th IEEE International Conference on Image Processing, October 12–15, 2008, San Diego, California, U.S.A.
dc.descriptionDOI: 10.1109/ICIP.2008.4711862
dc.description.abstractWe propose an approach for non-rigid tracking that represents objects by their set of distribution parameters. Compared to joint histogram representations, a set of parameters such as mixed moments provides a significantly reduced size representation. The discriminating power is comparable to that of the corresponding full high dimensional histogram yet at far less spatial and computational complexity. The proposed method is robust in the presence of noise and illumination changes, and provides a natural extension to the use of mixture models. Experiments demonstrate that the proposed method outperforms both full color mean-shift and global covariance searches.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectObject trackingen
dc.subjectMean shiften
dc.subjectRegion covarianceen
dc.subjectKernel density estimationen
dc.titleKernel-based high-dimensional histogram estimation for visual trackingen
dc.typeProceedingsen
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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