@InProceedings{Sixta-CVWW13,
  IS = { zkontrolovano 23 Jan 2014 },
  UPDATE  = { 2013-01-25 },
  author =      {Sixta, Tom{\'a}{\v s}},
  title =       {Star Convex Object Detection by the Infinite Shape Mixture Model},
  year =        {2013},
  pages =       {2-8},
  booktitle =   {CVWW 2013: Proceedings of the 18th Computer Vision Winter Workshop},
  publisher =   {Vienna University of Technology},
  address =     {Karlsplatz 13, Vienna, Austria},
  editor =      {Kropatsch, Walter G. and Ramachandran, Geetha and Torres, Fuensanta},
  book_pages =  {7},
  isbn =        {978-3-200-02943-9},
  month =       {February},
  day =         {4-6},
  venue =       {Hernstein, Austria},
  annote =      {Shape is an important feature of many object
    categories. In this paper we propose a Bayesian framework for
    detection of unknown number of objects based on their shape. The
    task is formulated as a minimization of Bayesian risk. The loss
    function is designed in such a way that the number of objects need
    not to be known or even bounded. We introduce a probability
    distribution over object states (number of objects and their
    poses) called Infinite Shape Mixture Model which is a modification
    of Rasmussen's Infinite Gaussian Mixture Model. Conditional
    posterior distributions are derived for all parameters of the
    model in order to make the inference feasible. Performance of the
    model is tested on two brief experiments.},
  keywords =   {Object detection, Bayesian inference},
  project  =   {GACR P202/12/2071},
}