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Proceedings Paper

M-SIFT: a new descriptor based on Legendre moments and SIFT
Author(s): Xin Zuo; Xiubin Dai; Limin Luo
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Paper Abstract

There are many feature descriptors that are insensitive to geometric transformations such as rotation and scale variation. However, most of them cannot effectively deal with blurred image which is a key problem in many real applications. In this paper, we propose a new feature descriptor that combines SIFT descriptor with combined blur, scale and rotation invariant Legendre moment (CBRSL). The proposed method inherits the advantage of SIFT and CBRSL which leads to invariance for scale, rotation and blur degradation simultaneously. We also show how this new descriptor is able to better represent the blur and geometric invariant feature descriptor in image registration. The experimental results validate the effectiveness of our method which is superior to SIFT methods.

Paper Details

Date Published: 12 January 2012
PDF: 7 pages
Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83501B (12 January 2012); doi: 10.1117/12.921074
Show Author Affiliations
Xin Zuo, Southeast Univ. (China)
Jiangsu Univ. of Science and Technology (China)
Xiubin Dai, Southeast Univ. (China)
Limin Luo, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 8350:
Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies
Safaa S. Mahmoud; Zhu Zeng; Yuting Li, Editor(s)

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