Stochastic optimization approach for entropic image alignment

In this paper, we introduce an image alignment method by maximizing a Tsallis entopy-based divergence using a modified simultaneous perturbation stochastic approximation algorithm. Due to its convexity property, this divergence measure 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 show the registration accuracy of the proposed approach in comparison with existing entropic image alignment techniques.