@InProceedings{Han02Fas,
  author    = "Robert Hanek and Thorsten Schmitt and Sebastian Buck and  Michael Beetz",
  title     = "{Fast Image-based Object Localization in Natural Scenes}",
  booktitle = "IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2002",
  series    = "Lausanne",
  year      = "2002",
  pages     = {116--122},
  bib2html_pubtype  = {Refereed Conference Paper},
  bib2html_rescat   = {Vision, Image Understanding},
  bib2html_groups   = {IAS, IU},
  bib2html_funding  = {BV},
  bib2html_keywords = {Vision},
  abstract ={In many robot applications, autonomous robots must be capable of localizing the objects they are to
             manipulate. In this paper we address the object localization problem by fitting a parametric curve
             model to the object contour in the image. The initial prior of the object pose is iteratively
             refined to the posterior distribution by optimizing the separation of the object and the background.
             The local separation criteria are based on local statistics which are iteratively computed from the
             object and the background region. No prior knowledge on color distributions is needed. Experiments
             show that the method is capable of localizing objects in a cluttered and textured scene even under
             strong variations of illumination. The method is able to localize a soccer ball within frame rate.}
}