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dc.contributor.authorSundaramoorthi, Ganeshen_US
dc.contributor.authorMennucci, Andrea C.en_US
dc.contributor.authorSoatto, Stefanoen_US
dc.contributor.authorYezzi, Anthonyen_US
dc.date.accessioned2013-09-04T20:36:47Z
dc.date.available2013-09-04T20:36:47Z
dc.date.issued2011-02
dc.identifier.citationSundaramoorthi, Ganesh; Mennucci, Andrea; Soatto, Stefano; Yezzi, Anthony, “A New Geometric Metric in the Space of Curves, and Applications to Tracking Deforming Objects by Prediction and Filtering,” SIAM Journal on Imaging Sciences, 4 (1), 109-145 (February 2011)en_US
dc.identifier.issn1936-4954
dc.identifier.urihttp://hdl.handle.net/1853/48781
dc.description©2011 Society for Industrial and Applied Mathematics. Permalink: http://dx.doi.org/10.1137/090781139en_US
dc.descriptionDOI: 10.1137/090781139en_US
dc.description.abstractWe define a novel metric on the space of closed planar curves which decomposes into three intuitive components. According to this metric, centroid translations, scale changes, and deformations are orthogonal, and the metric is also invariant with respect to reparameterizations of the curve. While earlier related Sobolev metrics for curves exhibit some general similarities to the novel metric proposed in this work, they lacked this important three-way orthogonal decomposition, which has particular relevance for tracking in computer vision. Another positive property of this new metric is that the Riemannian structure that is induced on the space of curves is a smooth Riemannian manifold, which is isometric to a classical well-known manifold. As a consequence, geodesics and gradients of energies defined on the space can be computed using fast closed-form formulas, and this has obvious benefits in numerical applications. The obtained Riemannian manifold of curves is ideal for addressing complex problems in computer vision; one such example is the tracking of highly deforming objects. Previous works have assumed that the object deformation is smooth, which is realistic for the tracking problem, but most have restricted the deformation to belong to a finite-dimensional group—such as affine motions—or to finitely parameterized models. This is too restrictive for highly deforming objects such as the contour of a beating heart. We adopt the smoothness assumption implicit in previous work, but we lift the restriction to finite-dimensional motions/deformations. We define a dynamical model in this Riemannian manifold of curves and use it to perform filtering and prediction to infer and extrapolate not just the pose (a finitely parameterized quantity) of an object but its deformation (an infinite-dimensional quantity) as well. We illustrate these ideas using a simple first-order dynamical model and show that it can be effective even on image sequences where existing methods fail.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectVisual object trackingen_US
dc.subjectShape analysisen_US
dc.subjectFiltering in the space of curvesen_US
dc.subjectSobolev-type metricsen_US
dc.titleA New Geometric Metric in the Space of Curves, and Applications To Tracking Deforming Objects by Prediction and Filteringen_US
dc.typeArticleen_US
dc.typePost-printen_US
dc.contributor.corporatenameUniversity of California, Los Angeles. Computer Science Dept.en_US
dc.contributor.corporatenameScuola normale superiore (Italy)en_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.publisher.originalSociety for Industrial and Applied Mathematicsen_US
dc.identifier.doi10.1137/090781139


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