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 signi cant 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.

• Type: Conference
• Year: 1994
• Where: ECCV
• Authors: Ramin Zabih, John Woodfill
• Keywords: Computer Vision, ECCV 1994, Local Image Structure, Local Intensity Values, Non-parametric Local Transforms, Normalized Correlation, Rank Transform