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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.