Multi-modal image registration using local frequency representation and computer-aided design (CAD) models

Given two images of roughly the same scene, image registration is the process of determining the transformation that nearly maps one image to another. While some efficient procedures have been developed lately, the registration of images acquired from sensors operating in different modalities is still a challenging problem. In general, such images have different gray level characteristics and the features in the two images to be registered are often not well preserved, rendering registration techniques such as those based on feature extraction and area correlation generally not efficient, and hence not feasible in all cases. In this paper, we propose a new algorithm for multi-sensor image registration based on using the local frequency representation of the images together with image representation of computer-aided design (CAD) models that permit region-of-interest-to-region-of-interest, ROI-to-ROI, space to solve image registration problem (instead of relying only on the captured images to solve image registration problem using image-to-image space). The key point underlying the proposed approach is the employment of local frequency representations of CAD models images to efficiently determine sets of matching points from the images to be registered, which in turn enables obtaining correspondence between these sets for estimating the transformation parameters. Performance evaluation results reported here indicate that the proposed technique is robust and yields promising results for multi-modal image registration.