
vrmatch(1)                   Khoros                    vrmatch(1)

PROGRAM
     vrmatch - Matching of regions in 2 images

SYNTAX
     vrmatch

     -i1            input image

     -i2            Input image

     -o             output image

     [-V]           Gives the version for vrmatch

     [-U]           Gives the usage for vrmatch

     [-P]           Prompts for command line options

     [-A [file1]]   Creates the answer file called vrmatch.ans or
                    file1

     [-a [file1]]   Uses vrmatch.ans or file1 as the answer file

DESCRIPTION
     WARNING: This is a temporary algorithm which has been used
     for stereo application prototyping. It can't be considered
     as the result of deep research on the topic.

     vrmatch analyses two input images supposed to be similar.
     The input images can either be the left and right images of
     a stereo image data base or successive images from an image
     data flow process.

     Theses images have to be previously preprocessed in the fol-
     lowing manner:

     Region_segmentation:
          This segmentation should give integer image containing
          connex regions.

     The matching process computes the probability of region Ni
     (1srt image) being region Mj (2nd image).

          P(Ni = Mj) =  1 - Surface( SXOR (Ni , Mj)) / (Surface
          (Ni) + Surface(Nj))

          SXOR is computed by translating Ni by a vector V such
          that the new centroid of Ni and Mj's one coincide.

          if SXOR(Ni,Mj) is small (the surfaces of Ni and Nj
          match) then the probability of Ni being Mj will be
          close to 1.

          On the contrary if SXOR(Ni,Mj) is relatively big (the
          two surfaces don't match because of different shape or
          size, etc) then the probability of Ni being Mj will be
          close to 0.

          In stereo or optical flow there is some difference in
          the shape between two regions representing the same
          thing. If these differences are too important (due to
          the segmentation, vlabel ,etc, or simply to images that
          are really different) then the probability for these
          regions to be the same object won't be that good. In
          that case a heuristic approach will decide if the two
          region can be matched or not. This heuristic will rely
          on the distance between the two centroids.

     The final result will be an image in which both image1 and
     image2 are inserted.
     The label of each matched region will be the same in the
     left and right part making it possible to see what has been
     recognized.

REQUIRED ARGUMENTS
     -i1    type: infile
            desc: input image

     -i2    type: infile
            desc: Input image

     -o     type: outfile
            desc: output image

OPTIONAL ARGUMENTS
     none

EXAMPLES
     vrmatch -i1 image1.xv -i2 image2.xv -o image.xv

SEE ALSO
     intro(1), verror(3), vgparms(3), vutils(3), lvrmatch(3)

RESTRICTIONS
     Work only with VFF_TYP_4_BYTE images that have been previ-
     ously processed by a labelling algorithm.

COPYRIGHT
     Copyright 1991, University of New Mexico.  All rights
     reserved.

Khoros                    Release: 1.0                          1

