IS = { zkontrolovano 10 Jan 2012 },
  UPDATE  = { 2011-12-29 },
  author =      {Sato, Tomokazu and Pajdla, Tom{\'a}{\v s} and Yokoya, Naokazu},
  title =       {Epipolar Geometry Estimation for Wide-baseline 
                 Omnidirectional Street View Images},
  year =        {2011},
  pages =       {56--63},
  book_pages =  {2204},
  isbn =        {978-1-4673-0063-6},
  booktitle =   {2011 IEEE International Conference on Computer
                 Vision Workshops (ICCV Workshops)},
  conference =  {The Second IEEE International Workshop on Mobile Vision},
  editor =      {Hua, Gang and Fu, Yun and Turk, Matthew and Pulli, Kari},
  publisher =   {IEEE Computer Society},
  address =     {Los Alamitos, USA},
  month =	{November},
  day =		{7},
  venue =	{Barcelona, Spain},
  annote =      {This paper presents a new robust method of
    epipolar-geometry estimation for omnidirectional images in
    wide-baseline settings, e.g. with Goolgle Street View images. The
    main idea is to learn new statistical geometric constraints that
    are derived from the feature descriptors into the model
    verification process of RANSAC. We show that these constraints
    provide correct epipolae geometry in very difficult
    situations. Robustness of epipolar-geometry estimation is
    quantitativelt evaluated for omnidirectional image pairs with
    variable baseline. The performance of the proposed method is
    demonstrated using the complete pipeline of structure-from-motion
    with real datasets of Google Street View images.},
  keywords =	{structure from motion, panoramic images, epipolar geometry},
  project =	{SGS10/190/OHK3/2T/13, FP7-SPACE-241523 PRoViScout, 
    MEXT (JAPAN) KAKENHI 23240024, MEXT (JAPAN) KAKENHI 23700208},
  authorship =  {30-50-20},
  note =        {CD-ROM},