Automatic marking point positioning of printed circuit boards based on template matching technique

Chung-Feng Jeffrey Kuo (), Chun-Han Tsai, Wei-Ren Wang and Han-Cheng Wu
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Chung-Feng Jeffrey Kuo: National Taiwan University of Science and Technology
Chun-Han Tsai: National Taiwan University of Science and Technology
Wei-Ren Wang: National Taiwan University of Science and Technology
Han-Cheng Wu: National Taiwan University of Science and Technology

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 2, No 13, 685 pages

Abstract: Abstract The traditional global template matching is time consuming, has low accuracy, and cannot be adapted to rotation and scale change. The template matching technique proposed in this study improves the time, accuracy and robustness for printed circuit boards (PCB). In order to shorten the image positioning time, the image preprocessing is implemented on PCB image and the image blocks are labeled to obtain the tagged image, and the feature vector is extracted and the marking point region image is selected. The feature vector with rotation change and scale change robustness is extracted from the tagged image after labeling in the PCB image by using artificial neural network, combined with image moments for training. The marking point region image in the PCB image is selected. The scale value of the marking point region image is estimated by parametric template vector matching. The deflection angle of marking point region image is calculated by Hough transform. The obtained scale value and deflection angle value are used for fast template matching to determine the marking point positioning. The three-dimensional (3D) parabolic curve fitting is implemented in marking point positioning and adjacent pixel position to reach the sub-pixel level accuracy. The experiment showed that the proposed template matching technique for the PCB image with or without noise or angle rotation, the average position accuracy error of each translated image is lower than 7 $$\upmu $$μm, and the error standard deviation is lower than 5 $$\upmu $$μm. The rotation angle error average and standard deviation of angular error of Hough transform are lower than 0.2$$^{\circ }$$∘, more accurate than orientation code (OC) method. The scale value estimation, relative error average and error standard deviation are lower than 0.004 and 0.006 for the image with or without noise. The average complete positioning time of PCB image at resolution of $$2500\times 2500$$2500×2500 is only 0.55 s, which is better than the 3.97 s of traditional global template matching. The results prove that the template matching technique of this study not only has sub-pixel level high accuracy and short computing time, but also has the robustness of rotation change and scale change interference. It can implement rapid, efficient and accurate positioning.

Keywords: Image registration; Neural network; Features vector; Parametric template vector matching; Fast template matching; 3D parabolic curve fitting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10845-016-1274-2

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