Non-Parametric Local Transforms for Computing Visual Correspondence

Abstract

We propose a new approach to the correspondence problem that makes use of non-parametric local transforms as the basis for correlation. Non-parametric local transforms rely on the relative ordering of local intensity values, and not on the intensity values themselves. Correlation using such transforms can tolerate a significant number of outliers. This can result in improved performance near object boundaries when compared with conventional methods such as normalized correlation. We introduce two non-parametric local transforms: the rank transform , which measures local intensity, and the census transform , which summarizes local image structure. We describe some properties of these transforms, and demonstrate their utility on both synthetic and real data.