Title: A fast eliminating translational deviation method in template matching

Authors: Fang Chen; Cunji Zhang; Jinfei Shi; Ping Chen

Addresses: School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China ' School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China ' School of Mechanical Engineering, Nanjing Institute of Technology, Nanjing, China ' School of Mechanical Engineering, Southeast University, Nanjing, Jiangsu, 211189, China

Abstract: To achieve accurate measurements of large scale machine parts with machine vision techniques, a new image matching method for image sequences is selected. And the matching method is one of the most critical influences on measuring speed and measuring precision. This paper aims at improving measuring 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 realised by searching the straight profile with chain code. The other is sequences matching to eliminate translational deviation. The new algorithm focuses on eliminating translational deviation. It makes good use of differences of nearest neighbour grey values. Two templates are taken separately from two neighbour 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 normalised cross correlation method. Experimental result demonstrates that the proposed algorithm is more effective than the NCC for image matching.

Keywords: machine vision; image sequences; image matching; straight profile; template matching; translational deviation; large scale machine parts; component measurement; measuring speed; nearest neighbour grey values.

DOI: 10.1504/IJCAT.2014.063915

International Journal of Computer Applications in Technology, 2014 Vol.50 No.1/2, pp.131 - 136

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 25 Jul 2014 *

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