IS = { zkontrolovano 15 Jan 2013 },
  UPDATE  = { 2012-12-27 },
  author = {Petricek, Tomas and Svoboda, Tomas},
  title = {Area-weighted Surface Normals for 3D Object Recognition},
  booktitle =  {ICPR '12: Proceedings of 21st International
                Conference on Pattern Recognition},
  address =    {10662 Los Vaqueros Circle, Los Alamitos, USA},
  publisher =  {IEEE Computer Society},
  isbn =       {978-4-9906441-0-9},
  book_pages = {3826},
  year = {2012},
  pages = {1492-1496},
  month = {November},
  day = {11-15},
  venue = {Tsukuba, Japan},
  annote = {This paper presents a method for feature-based 3D object recognition
    in cluttered scenes. It deals with the problem of non-uniform sampling
    density which is inherent in typical range sensing methods. We suggest
    a method operating on polygonal meshes which overcomes the problem
    by exploiting surface area in both establishing local frames and
    creating feature descriptors. The method is able to recognize even
    highly occluded objects and outperforms state of the art in terms
    of recognition rate on a standard publicly available dataset.},
  authorship = {50-50},
  keywords = {3D object recognition, local invariant features},
  project = {SGS11/125/OHK3/2T/13, GACR P103/10/1585, FP7-ICT-247870 NIFTi},