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dc.contributor.authorSiddiqi, Kaleem
dc.contributor.authorLauzière, Yves Bérubé
dc.contributor.authorTannenbaum, Allen R.
dc.contributor.authorZucker, Steven W.
dc.date.accessioned2009-05-21T20:14:18Z
dc.date.available2009-05-21T20:14:18Z
dc.date.issued1997-06
dc.identifier.citationKaleem Siddiqi, Yves Bérubé Lauzière, Allen Tannenbaum, Steven W. Zucker, "Area and Length Minimizing Flows for Shape Segmentation," IEEE Computer Society Conference on Pattern Recognition and Image Processing, 1997, 621-627en
dc.identifier.isbn0-8186-7822-4
dc.identifier.issn1063-6919
dc.identifier.urihttp://hdl.handle.net/1853/27940
dc.description©1997 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 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 17-19, 1997, San Juan, Puerto Rico.
dc.descriptionDOI: 10.1109/CVPR.1997.609390
dc.description.abstractSeveral active contour models have been proposed to unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes. The essential idea is to evolve a curve (in 20) or a surface (in 30) under constraints from image forces so that it clings to features of interest in an intensity image. Recently the evolution equation has. been derived from first principles as the gradient flow that minimizes a modified length functional, tailored io features such as edges. However, because the flow may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction. In this paper, we provide a justification for this term based on the gradient flow derived from a weighted area functional, with image dependent weighting factor. When combined with the earlier modified length gradient flow we obtain a pde which offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area flow may be used on its own, with significant computational savings.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectEdge detectionen
dc.subjectImage segmentationen
dc.subjectImage sequencesen
dc.subjectPartial differential equationsen
dc.titleArea and Length Minimizing Flows for Shape Segmentationen
dc.typeProceedingsen
dc.contributor.corporatenameYale University. Center for Computational Vision & Control
dc.contributor.corporatenameUniversity of Minnesota. Dept. of Electrical Engineering
dc.contributor.corporatenameMcGill University. Centre for Intelligent Machines
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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