Abstract
The registration of infrared and visible images is a common multi-modal image registration, which is widely used in military, remote sensing and other fields. After describing the registration of infrared and visible images, this paper mainly introduces the SIFT(Scale Invariant Feature Transform) algorithm and SURF(Speeded Up Robust Features) algorithm based on local invariant feature in image registration. First, we extract SIFT and SURF key points of infrared and visible images respectively. Next, we use approximation nearest neighbor search method based on k-d tree algorithm to match key points. Finally, in order to improve the matching accuracy, the RANSAC algorithm is used to eliminate the error matching points. The experiment shows that for these two algorithms, the number of key points in infrared image is obviously smaller than that of visible light image. For these two images, the SURF algorithm is better than the SIFT algorithm.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
