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Feature extraction and template matching algorithm classification for PCB fiducial verification

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: Automatic Optical Inspection (AOI) systems that are extensively used in the industry of Electronics Manufacturing Services (EMS), performs the inspection of Surface Mount Devices (SMD). One of the main tasks of such an AOI system is to align a given PCB to the parameters of the corresponding PCB positioning system by a process called fiducial alignment. However, no detailed analysis has been carried out so far on the methodologies that can be used to have a very precise identification of PCB fiducial points. In our research, we have implemented an AOI system for the inspection of soldering defects of Through Hole Technology (THT) solder joints, which can be integrated to a desktop soldering robotic platform. Such platforms are used in environments where no specific lighting conditions can be provided within a surrounded atmosphere. Therefore, an AOI system that is capable of performing fiducial alignment of any given PCB under varying lighting condition is highly preferred. In this paper, we have presented a detailed analysis on feature extraction and template matching algorithms that can be used to implement a very precise fiducial verification process under normal lighting condition. Design/methodology/approach: A detailed analysis and performance evaluation have been carried out in this paper on prominent image comparison algorithms that are extensively used in the field of image processing. Findings: According to the analysis carried out in this paper, it could be observed that the combination of feature extraction and template matching algorithms gives the best performance in PCB fiducial verification process. Research limitations/implications: This paper only presents the implementation of the front end of our proposed AOI system. The implemented methodologies for the automatic identification of soldering defects will be discussed in separate research papers. Practical implications: The methodologies presented in this paper can be effectively used to implement a very precise and robust PCB fiducial verification process that can be efficiently integrated to a desktop soldering robotic system. Originality/value: This research proposes a very accurate fiducial verification process that can be used under varying lighting conditions on a wide range of different PCB fiducial points.
Rocznik
Strony
14--32
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
  • Department of Electronics & Telecommunication, University of Moratuwa, Moratuwa 10400, Sri Lanka
  • Department of Electronics & Telecommunication, University of Moratuwa, Moratuwa 10400, Sri Lanka
Bibliografia
  • [1] G. Goldberg, ROI of Soldering Robot, Circuit Assembly Online Magaazine, http://www.circuitsassembly.com/ca/magazine/26341-automation-1609.html.
  • [2] PCB Assembly Equipment for Through Hole Assembly and Soldering, DDM Novastar, automated production systems, https://www.ddmnovastar.com/.
  • [3] C.L.S.C. Fonseka, J.A.K.S. Jayasinghe, Color Model Analysis for Solder Pad Segmentation on Printed Circuit Boards, International Journal of Scientific and Research Publications 6/11 (2016) 212-225.
  • [4] R. Katukam, P. Sindhoora, Image Comparison Methods & Tools: A Review, Proceedings of the 1st International Conference “Emerging trends in Information Technology” ETIT, 2015, 35-42.
  • [5] H. Ney, B. Leibe, Matching Algorithms for Image Segmentation, January, 2010.
  • [6] N. Praveen, D. Kumar, I. Bardwaj, An Overview on Template Matching Methodologies and its Applications, International Journal of Research in Computer and Communication Technology 2/10 (2013) 988-995.
  • [7] A. Banharnsakun, S. Tanathong, Object Detection based on Template Matching through use of best so far ABC, Computational Intelligence and Neuroscience 2014 (2014) ID: 919406 1-8.
  • [8] OpenCv, Introduction to SIFT, http://docs.opencv.org/3.3.0/da/df5/tutorial_py_sift_intro.html.
  • [9] OpenCV 2.4.13.3 documentation, Template Matching, http://docs.opencv.org/2.4/doc/tutorials/imgproc/histograms/template_matching/template_matching.html.
  • [10] G. Bradski, A. Kaehler, Learning OpenCV, O’Reilly, 2008.
  • [11] A. Aichert, Feature extraction techniques, Proceedings of the Camp Medical Seminar WS0708, 2008/
  • [12] Chu. Wu, Advanced Feature Extraction Algorithms For Automatic Fingerprint Recognition System”, PhD Thesis, State University of New York, New York, 2007.
  • [13] T. Kadir, D. Boukerroui, M. Brady, An analysis of the Scale Saliency algorithm, Robotics Research Laboratory, University of Oxford Publishing House, Oxford, 2003.
  • [14] I.R. Otero, M. Delbarico, Anatomy of the SIFT Method, Image Processing On Line 4 (2004) 370-396.
  • [15] H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-Up Robust Features (SURF), Proceedings of the European Conference on Computer Vision, 2006, 404-417.
  • [16] J.T. Pedersen, SURF: Feature detection & description, Q4 2001, http://cs.au.dk/~jtp/SURF/report.pdf.
  • [17] E. Oyallon, J. Rabin, An Analysis of the SURF Method, Image Processing On Line 5 (2015) 176-218.
  • [18] E. Rosten, T. Drummond, Machine learning for high-speed corner detection, Proceedings of the European Conference on Computer Vision ECCV 2006: Computer Vision - ECCV 2006, 430-443.
  • [19] J. Bollen, H. Van de Sompel, A. Hagberg, R. Chute, A Principal Component Analysis of 39 Scientific Impact Meaurees, PLoS One 4/6 (2009) 1-11.
  • [20] V.D. Calhoun, V.K. Potluru, R. Phlypo, R.F. Silva, B.A. Pearlmutter, A. Caprihan, S.M. Plis, T. Adali, Independent Component Analysis for Brain fMRI Does Indeed Select for Maximal Independence, PLoS One 8/8 (2013).
  • [21] D.G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints, International Journal of Computer Vision 60/2 (2004) 91-110.
  • [22] M. Basu, Gaussian-Based Edge-Detection Methods - A Survey, IEEE Transaction on Systems, Man and Cybernetics, Part C: Applications and Reviews 32/3 (2002) 252-260.
  • [23] T. Lindeberg, Edge Detection and Ridge Detection with Automatic Scale Selection, Proceedings of the IEEE Computer Society Conference: Computer Vision and Pattern Recognition, 1996.
  • [24] X. Yin, B.W.H. Ng, J. He, Y. Zhang, D. Abbott, Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping, PLoS One (2014), doi: 10.1371/journal.pone.0095943.
  • [25] B.S. Morse, Differential Geometry, Lecture 11, Brigham Young University, 1998-2000.
  • [26] D.E. Brown, The Hessian matrix: Eigenvalues, concavity, and curvature, BYU Idaho Department of Mathematics, April 2014.
  • [27] S. Ehsan, A.F. Clark, N. ur Rehman, K.D. McDonald-Maier, Integral Images Efficient Algorithms for Their Computation and Storage in Resource-Constrained Embedded Vision Systems, Sensors (Basel) 15/7 (2015) 16804-30.
  • [28] C.R. Molenkamp, Accuracy of Finite-Difference Methods Applied to the Advection Equation, Journal of Applied Meteorology 28 (1989).
  • [29] D.G. Viswanathan, Features from Accelerated Segment Test (FAST), http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL_COPIES/AV1011/AV1FeaturefromAcceleratedSegmentTest.pdf.
  • [30] M. Muja, David G. Lowe, Fast Approximate Nearest Neighbors With Automatic Algorithm Configuration, Computer Science Department, University of British Columbia, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.160.1721&rep=rep1&type=pdf.
  • [31] M. Calonder, V. Lepetit, C. Strecha, P. Fua, BRIEFF: Binary Robust Independent Elementary Feature, CVLab, EPFL, Lausanne, Switzerland.
  • [32] Digital Image Correlation: Overview of Principles and Software, SEM 2009, University of South California.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-db36bacc-2989-4d5e-b93b-3800272a6499
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