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dc.contributor.authorSandhu, Romeil
dc.contributor.authorDambreville, Samuel
dc.contributor.authorTannenbaum, Allen R.
dc.date.accessioned2009-06-19T18:31:33Z
dc.date.available2009-06-19T18:31:33Z
dc.date.issued2008-06
dc.identifier.citationRomeil Sandhu, Samuel Dambreville, Allen Tannenbaum, "Particle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamics," IEEE Conference on Computer Vision and Pattern Recognition, 2008, 1-8en
dc.identifier.isbn978-1-4244-2242-5
dc.identifier.issn1063-6919
dc.identifier.urihttp://hdl.handle.net/1853/28587
dc.description©2008 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
dc.descriptionPresented at the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 23-28, 2008, Anchorage, Alaska.
dc.descriptionCVPRW '08
dc.descriptionDOI: 10.1109/CVPR.2008.4587794
dc.description.abstractIn this paper, we propose a particle filtering approach for the problem of registering two point sets that differ by a rigid body transformation. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particle filter. In this work, we treat motion as a local variation in pose parameters obtained from running a few iterations of the standard Iterative Closest Point (ICP) algorithm. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer functions used to tackle the registration task. Thus, the novelty of our method is twofold: Firstly, we employ a particle filtering scheme to drive the point set registration process. Secondly, we increase the robustness of the registration performance by introducing a dynamic model of uncertainty for the transformation parameters. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity as well as maintains the temporal coherency of the state (no loss of information). Also, unlike most alternative approaches for point set registration, we make no geometric assumptions on the two data sets. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures or differing point densities in each sets, on challenging 2D and 3D registration tasks.en
dc.language.isoen_USen
dc.publisherGeorgia Institute of Technologyen
dc.subjectImage registrationen
dc.subjectMotion estimationen
dc.subjectPose estimationen
dc.subjectParticle filteringen
dc.titleParticle Filtering for Registration of 2D and 3D Point Sets with Stochastic Dynamicsen
dc.typeProceedingsen
dc.contributor.corporatenameGeorgia Institute of Technology. Dept. of Biomedical Engineering
dc.contributor.corporatenameEmory University. Dept. of Biomedical Engineering
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineering
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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