NDLI: A method of sift feature points matching for image mosaic

Content Provider	IEEE Xplore Digital Library
Author	Jie Zhao Hui-Juan Zhou Guo-Zun Men
Copyright Year	2009
Description	Author affiliation: College of Economics, Hebei University, Baoding 071002, China (Guo-Zun Men) \|\| College of Electronic and Information Engineering, Hebei University, Baoding 071002, China (Jie Zhao; Hui-Juan Zhou)
Abstract	This paper presents a approach of SIFT feature points matching for image mosaic. This method combines improved K-means clustering and simulated annealing algorithm to match SIFT feature points. Firstly, high robust points are extracted by SIFT algorithm; Secondly, cluster with the initial centers obtained by density function, and then optimize the results of clustering which are used as initial results of simulated annealing algorithm by perturbation; Thirdly, match feature points according to Nearest Neighbor algorithm; Finally, calculate the homography and realize image mosaic. This method does not need to traverse all feature points and avoid trapping in a local extremum. Experimental results prove that the method is only relative to geometric position of feature points, and is robust on scale invariant, arbitrary rotation and scaling.
Starting Page	2353
Ending Page	2357
File Size	790192
Page Count	5
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212159
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Simulated annealing Image registration Feature extraction Computational modeling Machine learning Cybernetics Educational institutions Robustness Nearest neighbor searches Image mosaic K-means SIFT Homography
Content Type	Text
Resource Type	Article

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