Optimization of the Histogram Intervals Number which Approximate Brightness Probability Distributions in Stochastic Image Alignment Based on Information Similarity Measures

Abstract

This article investigates the influence of histogram intervals number on the accuracy and speed of the stochastic algorithm needed to solve the image alignment problem. The mutual information of images is considered as a similarity measure. Histograms are used for approximation of brightness probability density distributions when finding the gradient of image mutual information. Experimental results are obtained for the Shannon, Renyi and Tsallis mutual information cases.