Mutual Information calculation on different appearances
This work addresses image alignment and matching for applications like medical imaging, but it is incremental as it applies existing methods without introducing new techniques.
The paper tackled the problem of image matching across different modalities by applying mutual information to measure similarity between moving and target images, and compared it with entropy and information-gain methods while investigating environmental effects through experiments and plots.
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.