Abstract:
Matching the Images, representing the same content but captured from di erent,
widely separated angels, is a fundamental computer vision challenge. SIFT(Scale
Invariant Feature Transform) is famous for matching images captured at di erent
depths (scales) with little view invariance. SIFT descriptor uses local information
around the keypoints to achieve this invariance. In this thesis we added view
invariance to these solid SIFT descriptors and used novel views approach to improve
matching. We tested our algorithm on di erent categories and found out that
matching can be improved by combining local and global descriptors. Our method
skips the noise of scene understanding, for example incorrect estimation of 3D box
etc., Finally, our approach is more e cient and has potential for real time robotics
scenarios.
