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dc.contributor.authorDambreville, Samuelen_US
dc.contributor.authorSandhu, Romeilen_US
dc.contributor.authorYezzi, Anthonyen_US
dc.contributor.authorTannenbaum, Allen R.en_US
dc.date.accessioned2010-06-28T16:58:49Z
dc.date.available2010-06-28T16:58:49Z
dc.date.issued2010-03-03
dc.identifier.citationSamuel Dambreville, Romeil Sandhu, Anthony Yezzi, and Allen Tannenbaum, "A Geometric Approach to Joint 2D Region-Based Segmentation and 3D Pose Estimation Using a 3D Shape Prior," SIAM journal on imaging sciences, Vol. 3, No. 1, 110–132en_US
dc.identifier.issn1936-4954
dc.identifier.urihttp://hdl.handle.net/1853/34057
dc.description©2010 Society for Industrial and Applied Mathematics. Permalink: http://dx.doi.org/10.1137/080741653en_US
dc.descriptionDOI: 10.1137/080741653en_US
dc.description.abstractIn this work, we present an approach to jointly segment a rigid object in a two-dimensional (2D) image and estimate its three-dimensional (3D) pose, using the knowledge of a 3D model. We naturally couple the two processes together into a shape optimization problem and minimize a unique energy functional through a variational approach. Our methodology differs from the standard monocular 3D pose estimation algorithms since it does not rely on local image features. Instead, we use global image statistics to drive the pose estimation process. This confers a satisfying level of robustness to noise and initialization for our algorithm and bypasses the need to establish correspondences between image and object features. Moreover, our methodology possesses the typical qualities of region-based active contour techniques with shape priors, such as robustness to occlusions or missing information, without the need to evolve an infinite dimensional curve. Another novelty of the proposed contribution is to use a unique 3D model surface of the object, instead of learning a large collection of 2D shapes to accommodate the diverse aspects that a 3D object can take when imaged by a camera. Experimental results on both synthetic and real images are provided, which highlight the robust performance of the technique in challenging tracking and segmentation applications.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectRegion-based segmentation and trackingen_US
dc.subjectThree-dimensional pose estimationen_US
dc.subjectThree-dimensionalen_US
dc.subjectShape priorsen_US
dc.subjectVariational methodsen_US
dc.subjectDifferential geometryen_US
dc.titleA Geometric Approach to Joint 2D Region-Based Segmentation and 3D Pose Estimation Using a 3D Shape Prioren_US
dc.typeArticleen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US
dc.publisher.originalSociety for Industrial and Applied Mathematicsen_US


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