2017/10/07 by Ebrahim Karami, Karami, Ebrahim, Mohamed Shehata +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #cs.CV
paper · pdf · doi:10.48550/arxiv.1710.02728
4 pages, 11 figures, In Proceedings of the 2015 Newfoundland Electrical and Computer Engineering Conference,St. johns, Canada, November, 2015
arxiv created 2018/03/13 · arxiv updated 2018/03/15
Image identification is one of the most challenging tasks in different areas of computer vision. Scale-invariant feature transform is an algorithm to detect and describe local features in images to further use them as an image matching criteria. In this paper, the performance of the SIFT matching algorithm against various image distortions such as rotation, scaling, fisheye and motion distortion are evaluated and false and true positive rates for a large number of image pairs are calculated and presented. We also evaluate the distribution of the matched keypoint orientation difference for each image deformation.