NDLI: A new method for image matching

Content Provider	IEEE Xplore Digital Library
Author	Fang Chen Cunji Zhang Jinfei Shi Ping Chen
Copyright Year	2012
Description	Author affiliation: School of Mechanical Engineering, Southeast University, Nanjing, China (Fang Chen; Cunji Zhang; Ping Chen) \|\| School of Mechanical Engineering, Nanjing Institute of Technology, China (Jinfei Shi)
Abstract	To achieve accurate measurements of large scale machine parts with machine vision techniques, a mosaic method for image sequences is selected. And the key technique of mosaic is image matching, which mainly affects measure speed and precision. This paper aims at improving measure speed. The proposed method bases on a straight profile. It mainly consists of two stages: One is adjusting image sequences to the same direction of coordinate axis to eliminate rotating deviation. It is realized by searching the straight profile with chain code. The other is sequences matching to eliminate shifting deviation. The new algorithm focuses on the eliminating shifting deviation. It makes good use of differences of nearest neighbor gray values. Two templates are taken separately from two neighbor sequences. Those templates complete matching by the minimum value of the sum of adjacent-pixel-difference. In experiments, the new method is contrasted with the classical algorithm of the normalized cross correlation method. Experimental result demonstrates that the proposed algorithm is more effective than the NCC for image mosaic.
Starting Page	190
Ending Page	193
File Size	2990960
Page Count	4
File Format	PDF
ISBN	9781467316439
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-28
Publisher Place	New Zealand
Access Restriction	Subscribed
Rights Holder	AUT University
Subject Keyword	template Image matching Machine vision Educational institutions Cameras Extraterrestrial measurements straight profile Image sequences Mechanical engineering image matching image sequences machine vision
Content Type	Text
Resource Type	Article

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