Non-parametric local transforms for computing visual correspondence

A new approach to the correspondence problem that makes use of non-parametric local transforms as the basis for correlation, which can result in improved performance near object boundaries when compared with conventional methods such as normalized correlation.

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.