Ordinal Measures for Image Correspondence 1

We present ordinal measures of association for image correspondence in the context of stereo. Linear correspondence measures like correlation and the sum of squared diier-ence between intensity distributions are known to be fragile. Ordinal measures which are based on relative ordering of intensity values in windows { rank permutations { have demonstrable robustness. By using distance metrics between two rank permutations, ordinal measures are deened. These measures are independent of absolute intensity scale and invariant to monotone transformations of intensity values like gamma variation between images. We have developed simple algorithms for their eecient implementation. Experiments suggest the superiority of ordinal measures over existing techniques under non-ideal conditions. These measures serve as a general tool for image matching that are applicable to other vision problems such as motion estimation and texture-based image retrieval.