A Radial Cumulative Similarity Transform for Robust Image Correspondence

We develop a local image-correspondence algorithm which performs well near occluding boundaries. Unlike traditional robust methods, our method can find correspondences when the only contrast present is the occluding boundary itself and when the sign of contrast along the boundary is possibly reversed. We define a new image transform which characterizes local image homogeneity, defined as an attribute value in a central region and a function describing the surrounding local similarity structure. In this paper we use radial similarity functions and color attributes; within each window we compute the central color and an image with the cumulative probability that color is unchanged along a ray from the center to a given point in the window. This representation is insensitive to structure outside an occluding boundary, but can model the boundary itself. We show comparative results tracking finger, mouth, and eye features.