Keypoint Descriptors for Matching Across Multiple Image Modalities and Non-Linear Intensity Variations

Abstract

In this paper, we investigate the effect of substantial inter-image intensity changes and changes in modality on the performance of keypoint detection, description, and matching algorithms in the context of image registration. In doing so, we modify widely-used keypoint descriptors such as SIFT and shape contexts, attempting to capture the insight that some structural information is indeed preserved between images despite dramatic appearance changes. These extensions include (a) pairing opposite-direction gradients in the formation of orientation histograms and (b) focusing on edge structures only. We also compare the stability of MSER, Laplacian-of-Gaussian, and Harris corner keypoint location detection and the impact of detection errors on matching results. Our experiments on multimodal image pairs and on image pairs with significant intensity differences show that indexing based on our modified descriptors produces more correct matches on difficult pairs than current techniques at the cost of a small decrease in performance on easier pairs. This extends the applicability of image registration algorithms such as the Dual-Bootstrap which rely on correctly matching only a small number of keypoints.