NDLI: Robust image registration of printed circuit boards using improved SIFT-PSO algorithm

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Robust image registration of printed circuit boards using improved SIFT-PSO algorithm

Robust visual tracking via two-stage binocular sparse learning

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Robust image registration of printed circuit boards using improved SIFT-PSO algorithm

Content Provider	IET Digital Library
Author	Dai, Linhui Guan, Qiao Liu, Hong
Abstract	Printed circuit board (PCB) layout is becoming high density, high performance, light, and short. In the automatic PCB defect detection system, image registration of PCB plays an important role. However, most of the traditional registration methods are inefficient, and cannot cope with the problems of image distortion, affine, noise, and so on. To address this issue, the authors propose an improved scale invariant feature transform (SIFT) feature extraction algorithm combined with particle swarm optimisation (PSO) to register the images of PCB which placed on a conveyor belt. The advantage of the presented approach is that the registration results are more robust and efficient by optimising the existing PCB image matching framework. The experimental results on the proposed PCB datasets show that the speed of the proposed method (improved SIFT-PSO) is faster than the traditional SIFT feature registration method, and the average computing time of processing single picture can be improved by 10 s, the registration accuracy can be improved by 3–4%. Compared with the experimental results of other algorithms, the root-mean-square error can be reduced to 0.5146 by using the proposed method. Thus, the proposed method (improved SIFT-PSO) is more accurate and robust in real-time inspection system of PCB.
Starting Page	1793
Ending Page	1797
Page Count	5
Volume Number	2018
e-ISSN	20513305
Issue Number	Issue 16, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2018/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8274
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-08-28
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Computer Vision And Image Processing Technique Conveyors Electronic Engineering Computing Feature Extraction Feature Extraction Algorithm Image Distortion Image Matching Image Registration Improved Scale Invariant Feature Inspection Integral Transforms Optical, Image And Video Signal Processing Optimisation Technique Particle Swarm Optimisation PCB Datasets PCB Defect Detection System PCB Image Matching Framework Printed Circuit Printed Circuit Boards Registration Accuracy Robust Image Registration Root Mean Square Error SIFT Feature Registration Method SIFT-PSO Algorithm Transform
Content Type	Text
Resource Type	Article

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