Nonextensive Entropic Image Registration

Abstract

We present an image registration approach by optimizing an information divergence based on the nonextensive Tsallis entopy. The optimization is carried out using a modified simultaneous perturbation stochastic approximation algorithm. And we show that this entropic divergence attains its maximum value when the conditional intensity probabilities between the reference image and the transformed target image are degenerate distributions. Experimental results are provided to demonstrate the registration accuracy of the proposed technique in comparison to existing entropic image alignment approaches. © 2009 Springer Berlin Heidelberg.