Bibtex entry

@InProceedings{chum-hashing-cvpr06,
  author =      {Chum, Ond{\vr}ej and Matas, Ji{\vr}{\'i}},
  title =       {Geometric Hashing with Local Affine Frames},
  booktitle =   {Proc. of Conference on Computer Vision and 
                 Pattern Recognition (CVPR)},
  address =     {Los Alamitos, USA} ,
  year =        {2006},
  month =       {June},
  day =         {17--22},
  isbn        = {0-7695-2597-0},
  publisher   = {IEEE Computer Society},
  book_pages  = {1313},
  pages    =    {879--884},
  psurl    =    {[pdf]},
  annote = { We propose a novel representation of local image
    structure and a matching scheme that are insensitive to a wide
    range of appearance changes. The representation is a collection of
    local affine frames that are constructed on outer boundaries of
    maximally stable extremal regions (MSERs) in an affine-covariant
    way. Each local affine frame is de- scribed by a relative location
    of other local affine frames in its neighborhood. The image is
    thus represented by quan- tities that depend only on the location
    of the boundaries of MSERs. Inter-image correspondences between
    local affine frames are formed in constant time by geometric
    hashing. Direct detection of local affine frames removes the
    require- ment of point-based hashing to establish reference frames
    in a combinatorial way, which has in the case of affine trans-
    form complexity that is cubic in the number of points.  Local
    affine frames, which are also the quantities represented in the
    hash table, occupy a 6D space and hence data collisions are less
    likely compared with 2D point hashing. Experimentally, the
    robustness of the method and its in- sensitivity to photometric
    changes is demonstrated on im- ages from different spectral bands
    of satellite sensor, on images of a transparent object and on
    images of an object taken during day and night. },
  keywords =    {MSER, hashing, two-view matching, wide-baseline stereo},
  editor      = {Fitzgibbon, Andrew and Taylor, Camillo and LeCun , Yan},
  venue       = {New York ,  USA  },
  volume      = { 1 },
}