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dc.contributor.authorAppia, Vikram
dc.contributor.authorGanapathy, Balaji
dc.contributor.authorYezzi, Anthony
dc.contributor.authorFaber, Tracy
dc.date.accessioned2014-11-18T22:24:41Z
dc.date.available2014-11-18T22:24:41Z
dc.date.issued2011-11
dc.identifier.citationAppia, V.; Ganapathy, B.; Yezzi, A.; & Faber, T. (2011). "Localized Principal Component Analysis Based Curve Evolution: A Divide and Conquer Approach". Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV 2011), November 6-13 2011, pp. 1981-1986.en_US
dc.identifier.urihttp://hdl.handle.net/1853/52834
dc.description© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.descriptionPresented at the 2011 IEEE International Conference on on Computer Vision (ICCV 2011), November 6-13 2014, Barcelona, Spain.
dc.descriptionDOI: 10.1109/ICCV.2011.6126469
dc.description.abstractWe propose a novel localized principal component analysis (PCA) based curve evolution approach which evolves the segmenting curve semi-locally within various target regions (divisions) in an image and then combines these locally accurate segmentation curves to obtain a global segmentation. The training data for our approach consists of training shapes and associated auxiliary (target) masks. The masks indicate the various regions of the shape exhibiting highly correlated variations locally which may be rather independent of the variations in the distant parts of the global shape. Thus, in a sense, we are clustering the variations exhibited in the training data set. We then use a parametric model to implicitly represent each localized segmentation curve as a combination of the local shape priors obtained by representing the training shapes and the masks as a collection of signed distance functions. We also propose a parametric model to combine the locally evolved segmentation curves into a single hybrid (global) segmentation. Finally, we combine the evolution of these semi-local and global parameters to minimize an objective energy function. The resulting algorithm thus provides a globally accurate solution, which retains the local variations in shape. We present some results to illustrate how our approach performs better than the traditional approach with fully global PCA.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectDivide and conquer methodsen_US
dc.subjectImage segmentationen_US
dc.subjectPattern clusteringen_US
dc.subjectPrincipal component analysisen_US
dc.titleLocalized Principal Component Analysis based Curve Evolution: A Divide and Conquer Approachen_US
dc.typePost-printen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.contributor.corporatenameEmory University
dc.publisher.originalInstitute of Electrical and Electronics Engineers
dc.embargo.termsnullen_US


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