Two Multimodal Image Registration Approaches for Positioning Purposes

This report is the result of a master thesis made by two students at Linköping University. The aim was to find an image registration method for visual and infrared images and to find an error measure for grading the registration performance. In practice this could be used for position determination by registering the infrared image taken at the current position to a set of visual images with known positions and determining which visual image matches the best. Two methods were tried, using different image feature extractors and different ways to match the features. The first method used phase information in the images to generate soft features and then minimised the square error of the optical flow equation to estimate the transformation between the visual and infrared image. The second method used the Canny edge detector to extract hard features from the images and Chamfer distance as an error measure. Both methods were evaluated for registration as well as position determination and yielded promising results. However, the performance of both methods was image dependent. The soft edge method proved to be more robust and precise and worked better than the hard edge method for both registration and position determination