Fast entropy-based nonrigid registration

Abstract

Computer vision tasks such as learning, recognition, classification or segmentation applied to spatial data often requires spatial normalization of repeated features and structures. Spatial normalization, or in other words, image registration, is still a big hurdle for the image processing community. Its formulation often relies on the fact that correspondence is achieved when a similarity measure is maximized. This paper presents a novel similarity measuring technique based on a coupling function inside a template matching framework. It allows using any entropy-based similarity metric, which is crucial for registration using different acquisition devices. Results are presented using this technique on a multiresolution incremental scheme.