• Combining Self Training and Active Learning for Video Segmentation 

      Fathi, Alireza; Balcan, Maria Florina; Ren, Xiaofeng; Rehg, James M. (Georgia Institute of Technology, 2011-09)
      This work addresses the problem of segmenting an object of interest out of a video. We show that video object segmentation can be naturally cast as a semi-supervised learning problem and be efficiently solved using ...
    • Robust 3D visual tracking using particle filtering on the SE(3) group 

      Choi, Changhyun; Christensen, Henrik I. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2011-05)
      In this paper, we present a 3D model-based object tracking approach using edge and keypoint features in a particle filtering framework. Edge points provide 1D information for pose estimation and it is natural to consider ...
    • Slam with expectation maximization for moveable object tracking 

      Rogers, John G.; Trevor, Alexander J. B.; Nieto-Granda, Carlos; Christensen, Henrik I. (Georgia Institute of TechnologyInstitute of Electrical and Electronics Engineers, 2010-10)
      The goal of simultaneous localization and mapping (SLAM) is to compute the posterior distribution over landmark poses. Typically, this is made possible through the static world assumption - the landmarks remain in the same ...