Mutual Information calculation on different appearances

TL;DR

This paper explores the application of mutual information for image matching across different modalities, comparing it with entropy and information-gain methods, and analyzing environmental effects on mutual information.

Contribution

It introduces the use of mutual information for image similarity evaluation across various environments and compares it with other dependency measures.

Findings

Mutual information effectively measures image similarity across different modalities.

Environmental factors influence mutual information values.

Comparison shows mutual information has advantages over entropy and information-gain methods.

Abstract

Mutual information has many applications in image alignment and matching, mainly due to its ability to measure the statistical dependence between two images, even if the two images are from different modalities (e.g., CT and MRI). It considers not only the pixel intensities of the images but also the spatial relationships between the pixels. In this project, we apply the mutual information formula to image matching, where image A is the moving object and image B is the target object and calculate the mutual information between them to evaluate the similarity between the images. For comparison, we also used entropy and information-gain methods to test the dependency of the images. We also investigated the effect of different environments on the mutual information of the same image and used experiments and plots to demonstrate.