|The authors present a qualitative
and quantitative comparison of various similarity measures that form the
kernel of common area-based stereo-matching systems. The authors compare
classical difference and correlation measures as well as nonparametric measures
based on the rank and census transforms for a number of outdoor images.
For robotic applications, important considerations include robustness to
image defects such as intensity variation and noise, the number of false
matches, and computational complexity. In the absence of ground truth data,
the authors compare the matching techniques based on the percentage of matches
that pass the left-right consistency test. The authors also evaluate the
discriminatory power of several match validity measures that are reported
in the literature for eliminating false matches and for estimating match
confidence. For guidance applications, it is essential to have an estimate
of confidence in the three-dimensional points generated by stereo vision.
Finally a new validity measure, the rank constraint, is introduced that
is capable of resolving ambiguous matches for rank transform--based matching.