Information Theoretic Similarity Measures for Robust Image Matching : Multimodal Imaging - Infrared and Visible light

Abstract: Abstract This study aimed to investigate the applicability of three different information theoretic similarity measures in image matching, mutual information (MI), cross-cumulative residual entropy (CCRE) and sum of conditional variances (SCV). An experiment was conducted to assess the impact on the performances of the similarity measures when dealing with multimodality, in this case in the context of infrared and visible light. This was achieved by running simulations of four different scenarios using images taken in infrared and visible light, and additionally with variations in amount of details to create different experimental setups. Namely experimental setup A: unimodal data sets with more and less details and experimental setup B: multimodal datasets with more and less details. The result showed that the concept of multimodality gives a statistically significant effect on the performances of all similarity measures. Observations were made that the similarity measures performances also, when trying to match images with different amount of details, differed from each other. This provided a basis for judgement on what measure to use as to give as clear and sound results as possible depending on the variation of detail amount in the data. With this study, it was concluded that the similarity measure CCRE gave the most clear and sound results in the context of multimodality concerning infrared and visible light for both cases of more or less details. Even though the other similarity measures performed well in some cases, CCRE would be to recommend as observed by this study. Keywords : Image matching, image registration, information theoretic similarity measures, multimodal imaging, similarity measures, MI, CCRE, SCV, infrared, visible light.