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dc.contributor.authorManay, Siddharthen_US
dc.contributor.authorCremers, Danielen_US
dc.contributor.authorHong, Byung-Wooen_US
dc.contributor.authorSoatto, Stefanoen_US
dc.contributor.authorYezzi, Anthonyen_US
dc.date.accessioned2013-09-06T20:35:30Z
dc.date.available2013-09-06T20:35:30Z
dc.date.issued2006-10
dc.identifier.citationSiddharth Manay, Daniel Cremers, Byung-Woo Hong, Anthony J. Yezzi Jr., and Stefano Soatto, "Integral Invariants for Shape Matching," IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (10), 1602-1618 (October 2006)en_US
dc.identifier.issn0162-8828
dc.identifier.urihttp://hdl.handle.net/1853/48830
dc.description©2006 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.descriptionDOI: 10.1109/TPAMI.2006.208en_US
dc.description.abstractFor shapes represented as closed planar contours, we introduce a class of functionals which are invariant with respect to the Euclidean group and which are obtained by performing integral operations. While such integral invariants enjoy some of the desirable properties of their differential counterparts, such as locality of computation (which allows matching under occlusions) and uniqueness of representation (asymptotically), they do not exhibit the noise sensitivity associated with differential quantities and, therefore, do not require presmoothing of the input shape. Our formulation allows the analysis of shapes at multiple scales. Based on integral invariants, we define a notion of distance between shapes. The proposed distance measure can be computed efficiently and allows warping the shape boundaries onto each other; its computation results in optimal point correspondence as an intermediate step. Numerical results on shape matching demonstrate that this framework can match shapes despite the deformation of subparts, missing parts and noise. As a quantitative analysis, we report matching scores for shape retrieval from a database.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectIntegral invariantsen_US
dc.subjectShapeen_US
dc.subjectShape matchingen_US
dc.subjectShape distanceen_US
dc.subjectShape retrievalen_US
dc.titleIntegral Invariants for Shape Matchingen_US
dc.typeArticleen_US
dc.contributor.corporatenameLawrence Livermore National Laboratory. Electronics Engineering Technologies Divisionen_US
dc.contributor.corporatenameUniversity of California, Los Angeles. Computer Science Dept.en_US
dc.contributor.corporatenameUniversity of California, Los Angeles. Computer Science Dept.en_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineersen_US
dc.identifier.doi10.1109/TPAMI.2006.208


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