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    EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence 

    Dellaert, Frank; Seitz, Steven M.; Thorpe, Charles E.; Thrun, Sebastian (Georgia Institute of Technology, 2003)
    Learning spatial models from sensor data raises the challenging data association problem of relating model parameters to individual measurements. This paper proposes an EM-based algorithm, which solves the model learning ...
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    An MCMC-based Particle Filter for Tracking Multiple Interacting Targets 

    Khan, Zia; Balch, Tucker; Dellaert, Frank (Georgia Institute of Technology, 2003)
    We describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause ...
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    A Sample of Monte Carlo Methods in Robotics and Vision 

    Dellaert, Frank (Georgia Institute of Technology, 2003-12)
    Approximate inference by sampling from an appropriately constructed posterior has recently seen a dramatic increase in popularity in both the robotics and computer vision community. In this paper, I will describe a number ...
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    Multitarget Tracking with Split and Merged Measurements 

    Khan, Zia; Balch, Tucker; Dellaert, Frank (Georgia Institute of Technology, 2005-06)
    In many multitarget tracking applications in computer vision, a detection algorithm provides locations of potential targets. Subsequently, the measurements are associated with previously estimated target trajectories in ...

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    AuthorDellaert, Frank (4)Balch, Tucker (2)Khan, Zia (2)Seitz, Steven M. (1)Thorpe, Charles E. (1)Thrun, Sebastian (1)Subject
    Computer vision (4)
    Markov chain Monte Carlo (4)
    Approximate inference (1)Correspondence Problem (1)Data association (1)Detection (1)Efficient sampling (1)Expectation-maximization (1)Markov random fields (1)Measurements (1)... View MoreDate Issued2005 (1)2003 (3)
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