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dc.contributor.authorRathi, Yogesh
dc.contributor.authorTannenbaum, Allen R.
dc.contributor.authorMichailovich, Oleg V.
dc.date.accessioned2009-07-24T16:39:30Z
dc.date.available2009-07-24T16:39:30Z
dc.date.issued2007-06
dc.identifier.citationYogesh Rathi, Allen Tannenbaum, Oleg Michailovich, "Segmenting Images on the Tensor Manifold," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007, 1 - 8en
dc.identifier.isbn1-4244-1180-7
dc.identifier.issn1063-6919
dc.identifier.urihttp://hdl.handle.net/1853/29237
dc.description©2007 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.descriptionDOI: 10.1109/CVPR.2007.383010
dc.descriptionPresented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 17-22 June 2007, Minneapolis, MN.
dc.description.abstractIn this note, we propose a method to perform segmentation on the tensor manifold, that is, the space of positive definite matrices of given dimension. In this work, we explicitly use the Riemannian structure of the tensor space in designing our algorithm. This structure has already been utilized in several approaches based on active contour models which separate the mean and/or variance inside and outside the evolving contour. We generalize these methods by proposing a new technique for performing segmentation by separating the entire probability distributions of the regions inside and outside the contour using the Bhattacharyya metric. In particular, this allows for segmenting objects with multimodal probability distributions (on the space of tensors). We demonstrate the effectiveness of our algorithm by segmenting various textured images using the structure tensor. A level set based scheme is proposed to implement the curve flow evolution equation.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectImage segmentationen
dc.subjectImage textureen
dc.subjectMatrix algebraen
dc.subjectProbabilityen
dc.titleSegmenting Images on the Tensor Manifolden
dc.typeProceedingsen
dc.contributor.corporatenameUniversity of Waterloo
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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