Abstract
An improved Scale Invariable Feature Transformation(SIFT) matching algorithm based on second moment matrix was presented to solve the problems that SIFT results in low matching ratio when view point of image changed.Feature points were detected in scale space and each feature point neighborhood was estimated by affine second moment matrix,then feature vectors were computed by dominant orientation assignment to each feature point based on elliptical neighboring region,finally the feature vectors were matched by using Euclidean distance.The experimental results show that this algorithm is as robust as SIFT,also acquires good performance on affine invariance of view point change,and improves matching performance greatly.
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.
