Title:
A curve evolution approach to smoothing and segmentation using the Mumford-Shah functional

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dc.contributor.author Tsai, Andy en_US
dc.contributor.author Yezzi, Anthony en_US
dc.contributor.author Willsky, Alan S. en_US
dc.contributor.corporatename Massachusetts Institute of Technology. Dept. of Electrical and Computer Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Electrical and Computer Engineering en_US
dc.date.accessioned 2013-09-10T14:37:01Z
dc.date.available 2013-09-10T14:37:01Z
dc.date.issued 2000-06
dc.description ©2000 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_US
dc.description DOI: 10.1109/CVPR.2000.855808 en_US
dc.description.abstract In this work, we approach the classic Mumford-Shah problem from a curve evolution perspective. In particular we let a given family of curves define the boundaries between regions in an image within which the data are modeled by piecewise smooth functions plus noise as in the standard Mumford-Shah functional. The gradient descent equation of this functional is then used to evolve the curve. Each gradient descent step involves solving a corresponding optimal estimation problem which connects the Mumford-Shah functional and our curve evolution implementation with the theory of boundary-value stochastic processes. The resulting active contour model, therefore, inherits the attractive ability of the Mumford-Shah technique to generate, in a coupled Mumford-Shah a smooth reconstruction of the image and a segmentation as well. We demonstrate applications of our method to problems in which data quality is spatially varying and to problems in which sets of pixel measurements are missing. Finally, we demonstrate a hierarchical implementation of our model which leads to a fast and efficient algorithm capable of dealing with important image features such as triple points. en_US
dc.identifier.citation A. Tsai, A. Yezzi, and A. Willsky, “A curve evolution approach to smoothing and segmentation using the Mumford-Shah functional,” 2000 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 119-124 (June 2000) en_US
dc.identifier.doi 10.1109/CVPR.2000.855808
dc.identifier.isbn 0-7695-0662-3
dc.identifier.issn 1063-6919
dc.identifier.uri http://hdl.handle.net/1853/48925
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers en_US
dc.subject Mumford-Shah functional en_US
dc.subject Active contour model en_US
dc.subject Curve evolution en_US
dc.subject Partial differential equations en_US
dc.subject Smoothing methods en_US
dc.title A curve evolution approach to smoothing and segmentation using the Mumford-Shah functional en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Yezzi, Anthony
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAuthorOfPublication 53ee63a2-04fd-454f-b094-02a4601962d8
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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