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dc.contributor.authorSchindler, Grant
dc.contributor.authorDellaert, Frank
dc.date.accessioned2011-04-01T17:58:05Z
dc.date.available2011-04-01T17:58:05Z
dc.date.issued2004-06
dc.identifier.citationSchindler, G., & Dellaert, F. (2004). “Atlanta World: an Expectation Maximization Framework for Simultaneous Low-Level Edge Grouping and Camera Calibration in Complex Man-Made Environments." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2004), 27 June-2 July 2004, I:203-I:209.en_US
dc.identifier.issn1063-6919
dc.identifier.urihttp://hdl.handle.net/1853/38365
dc.description©2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.en_US
dc.descriptionPresented at the 2004 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27 June-2 July 2004, Washington, D.C.
dc.descriptionDOI: 10.1109/CVPR.2004.1315033
dc.description.abstractEdges in man-made environments, grouped according to vanishing point directions, provide single-view constraints that have been exploited before as a precursor to both scene understanding and camera calibration. A Bayesian approach to edge grouping was proposed in the Manhattan World paper by Coughlan and Yuille, where they assume the existence of three mutually orthogonal vanishing directions in the scene. We extend the thread of work spawned by Coughlan and Yuille in several signi cant ways. We propose to use the expectation maximization (EM) algorithm to perform the search over all continuous parameters that in uence the location of the vanishing points in a scene. Because EM behaves well in high-dimensional spaces, our method can optimize over many more parameters than the exhaustive and stochastic algorithms used previously for this task. Among other things, this lets us optimize over multiple groups of orthogonal vanishing directions, each of which induces one additional degree of freedom. EM is also well suited to recursive estimation of the kind needed for image sequences and/or in mobile robotics. We present experimental results on images of Atlanta worlds, complex urban scenes with multiple orthogonal edge-groups, that validate our approach. We also show results for continuous relative orientation estimation on a mobile robot.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectCamera calibrationen_US
dc.subjectEdge groupingen_US
dc.subjectExpectation maximizationen_US
dc.subjectMan-made environmentsen_US
dc.subjectMobile robotsen_US
dc.subjectRecursive estimationen_US
dc.subjectSingle-view constraintsen_US
dc.subjectVanishing pointen_US
dc.titleAtlanta World: An Expectation Maximization Framework for Simultaneous Low-level Edge Grouping and Camera Calibration in Complex Man-made Environmentsen_US
dc.typePost-printen_US
dc.typeProceedings
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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