@InProceedings{Cech-CVPR-2008,
  IS = { zkontrolovano 16 Jan 2009 },
   UPDATE  = { 2008-08-28 },
   author   = { Jan {\v C}ech and Ji{\v r}{\' i} Matas and 
                Michal Per{\v d}och },
   title = {Efficient Sequential Correspondence Selection by Cosegmentation},
   booktitle = {CVPR 2008: Proceedings of the 2008 IEEE Computer Society Conference on
                Computer Vision and Pattern Recognition},
   year = {2008},
   month = {June},
   day = {24--26},
   venue = {Anchorage, Alaska, USA},
   pages = {1020--1027},
   project = {1ET101210406, FP6-IST-027113, ICT-215078 DIPLECS, specific research},
   authorship = {50-30-20},
   keywords = {correspondence, verification, dense stereo, growing, 
               sequential decison, learning, SVM, wide-baseline  stereo,
               image retrieval, SIFT, RANSAC},
   annote = {In many retrieval, object recognition and wide baseline
     stereo methods, correspondences of interest points (distinguished
     regions, transformation covariant points) are established
     possibly sublinearly by matching a compact descriptor such as
     SIFT. We show that a subsequent cosegmentation process coupled
     with a quasi-optimal sequential decision process leads to a
     correspondence verification procedure that has (i) high precision
     (is highly discriminative) (ii) good recall and (iii) is fast.
     The sequential decision on the correctness of a correspondence is
     based on trivial attributes of a modified dense stereo matching
     algorithm. The attributes are projected on a prominent
     discriminative direction by SVM.  Wald's sequential probability
     ratio test is performed for SVM projection computed on
     progressively larger co-segmented regions.  Experimentally we
     show that the process significantly outperforms the standard
     correspondence selection process based on SIFT distance ratios on
     challenging matching problems.},
 psurl = {[PDF] },
 ISBN = {978-1-4244-2242-5},
 issn = {1063-6919},
 book_pages =  {2954},
 publisher = {Omnipress},
 address =  {Madison, USA},
 note = {cd-rom},
}