An evolutionary measure for image matching

We present an evolutionary measure for image matching that is based on the Ulam's distance. Given two strings, the Ulam's distance is the smallest number of mutations, insertions and deletions that can, be made within the strings such that the resulting substrings are identical. We reinterpret the Ulam's distance with respect to permutations that represent window intensities expressed on an ordinal scale. The motivation for using this measure is twofold: it not only gives a robust measure of correlation between windows but also helps in, identifying pixels that contribute to the agreement (or disagreement) between the windows. We investigate computational issues for efficient implementation of the measure. Experiments suggest the utility of the Ulam's distance in applications like stereo.