Sparse Modeling of Intrinsic Correspondences

dc.contributor.authorPokrass, Jonathanen_US
dc.contributor.authorBronstein, Alexander M.en_US
dc.contributor.authorBronstein, Michael M.en_US
dc.contributor.authorSprechmann, Pabloen_US
dc.contributor.authorSapiro, Guillermoen_US
dc.contributor.editorI. Navazo, P. Poulinen_US
dc.date.accessioned2015-02-28T15:26:20Z
dc.date.available2015-02-28T15:26:20Z
dc.date.issued2013en_US
dc.description.abstractWe present a novel sparse modeling approach to non-rigid shape matching using only the ability to detect repeatable regions. As the input to our algorithm, we are given only two sets of regions in two shapes; no descriptors are provided so the correspondence between the regions is not know, nor we know how many regions correspond in the two shapes. We show that even with such scarce information, it is possible to establish very accurate correspondence between the shapes by using methods from the field of sparse modeling, being this, the first non-trivial use of sparse models in shape correspondence. We formulate the problem of permuted sparse coding, in which we solve simultaneously for an unknown permutation ordering the regions on two shapes and for an unknown correspondence in functional representation. We also propose a robust variant capable of handling incomplete matches. Numerically, the problem is solved efficiently by alternating the solution of a linear assignment and a sparse coding problem. The proposed methods are evaluated qualitatively and quantitatively on standard benchmarks containing both synthetic and scanned objects.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12066en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12066en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectI.4.8 [Image Processing And Computer Vision]en_US
dc.subjectScene Analysisen_US
dc.subjectShapeen_US
dc.titleSparse Modeling of Intrinsic Correspondencesen_US
Files