臺灣博碩士論文加值系統

板數計數在印刷電路板產線品質控管中扮演著至關重要的角色。通常這些堆疊的基板是由PCB廠人工計數，然而，這種計數方式無法實現成本、勞力和時間效率。儘管近期有應用電腦視覺自動板數測量的相關研究，但在大多數情況下，這些方法對於應對如光線變化不均勻的外部干擾、PCB板上不規則的雜訊與大量板數計數皆有需改善之處。在本論文中，我們研究如何解決五個關鍵挑戰：PCB背景分離、特徵增強、濾除影像高雜訊區域、提取穩定脊線分佈影像，以及自動板數測量方法。為了擷取影像中的PCB板並使之呈現垂直型態以利於計數，我們研究了一種PCB與背景分離的方法。接著，我們提出了一種自適應特徵增強方法，利用色彩空間轉換和形態學處理，實現降低雜訊和影像復原。基於此增強結果，我們設計了一個clean ratio指標，用於評估圖像中各個區域的雜訊比例，提取雜訊較少的區域以進行下一步處理。我們的雜訊過濾方法結合了紋理分析、影像結構分析與形狀描述，這些將在隨後的章節中闡述。我們進一步設計了一種能衡量PCB板間是否有穩定間距的演算法，這種方法也同時估計PCB板寬並衡量出每一區域的stable ratio。在最後步驟中的板數計數方法，我們結合了頻率域和空間域影像處理方法實現計數，這種方法可以在自動推測與調校PCB板寬，並以此推斷出可信度高的板數。相較於大多數先前的數板方法，我們的方法相較於現有技術有顯著改進，更能克服多種型態的雜訊以實現計數精確度。

Batch measurement plays an important role in quality control in the assembly line of printed circuit boards (PCBs). The stacked substrates counting which is reckoned up by employees in the PCB factory. This form of counting, however, cannot achieve cost, labour and time efficiency. Yet recent works have shown that automatic batch measurement can cope with by computer vision techniques more accurate and efficient. These methods, in most cases, neither perform robustly with a wide variety of interference and abnormalities such as foreign contamination and illumination changes nor processing a huge amount of stacks. In this paper, we study how to address five critical challenges for these tasks: PCBs-background segmentation, feature enhancement, filtering clean image segments, extract stable ridge-line distribution image, and automatic batch measurement approach. To recognise and localise the PCBs on the image, a semantic PCBs segmentation method is utilised. This method extracted the PCBs from the camera image and enables the slanted source image to remain vertically satisfy the ensuing counting approaches. We then propose an adaptive feature enhancement approach that embraces colour space conversion and mathematical morphology to achieve noise reduction and image restoration. Based on this enhancement result, we present an indicator, called clean ratio, to estimate how many noises a region of the image exists, then extract the clean region for the next step. Our noise filtering approaches combine texture analysis, structural analysis and shape descriptors which will unfold in the ensuing chapters. We further introduce an algorithm which is used to discover the stability of the recurrence frequency for ridge-lines distance whereas this method could estimate the board width and mapping a stable ratio for each region. A frequency and spatial domain image processing approaches are eventually implemented in the stacked substrates counting approach. This approach automatically fine-tuning the board size for each region of the image and suggests the highest confidence board numbers. Our method obtains significant improvements over the state-of-the-art on most of the previous approaches whilst grapples with abnormalities noise to achieve measurement meticulously.

摘要 iii
ABSTRACT v
致謝 vii
Table of Contents ix
List of Tables xii
List of Figures xiv
Chapter 1. Introduction 1
Chapter 2. Related Work 5
2.1 Related Literature On Batch Measurement Using Computer Vision 5
2.2 Image Enhancement, Noise Reduction and Image Restoration 6
Chapter 3. Methodology 7
3.1 Feature Enhancement 8
3.1.1 Image Morphology 9
3.1.2 Shadow Remover 10
3.1.3 Colour Space Conversions and Colour Balance 11
3.2 Filtering Clean Image Crops 16
3.2.1 Texture Analysis using Gabor Filters 18
3.2.2 Binary Segmentation 22
3.2.3 Otsu’s Algorithm 23
3.2.4 Adaptive Threshold 23
3.2.5 Gabor Binary Image Segmentation and Pixel Intensity Evaluation 25
3.2.6 Structural Analysis and Shape Descriptors 26
3.2.7 Connected Components with Statistic 26
3.2.8 K-means Clustering 26
3.3 Stable Ridge-line Distribution Images Extraction 28
3.3.1 Image Restoration with Motion Deblur Filter 30
3.3.2 Ringing Effect / Edge Effect 32
3.3.3 Further Discuss the Problem of Edge Effect: Circular Convolution 32
3.3.4 Binary Segmentation with K-means 34
3.3.5 Stable Image Ratio 34
3.4 The Stacked Substrates Counting Approach 36
3.4.1 Deblur with Bilateral Filter Smoothing 37
3.4.2 Image Smoothing with Gaussian Convolution 37
3.4.3 Edge-preserving Filtering with the Bilateral Filter 38
3.4.4 Gap Suppression 40
3.4.5 Line Extraction 41
3.4.6 Tuning Best Board Width with Padding Line 42
3.4.7 Board Numbers Inference 43
Chapter 4. Experiments and Analysis 44
4. Functional Comparison between Ours and Others’ Work 44
4.2 Hyper Parameters 46
4.3 Implementations 57
4.4 Limitations 76
Chapter 5. Conclusions and Future Work 77
5.1 The Main Contributions 77
5.2 Trends and Future Work 77
Reference 79
Appendix 87

[1]J. Canny, "A computational approach to edge detection," in Readings in computer vision: Elsevier, 1987, pp. 184-203.
[2]R. O. Duda and P. E. Hart, "Use of the Hough transformation to detect lines and curves in pictures," Sri International Menlo Park Ca Artificial Intelligence Center, 1971.
[3]R. G. Von Gioi, J. Jakubowicz, J.-M. Morel, and G. Randall, "LSD: A fast line segment detector with a false detection control," IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 4, pp. 722-732, 2008.
[4]D.-A. CHEN, Line Restoring Algorithm for PCB Detection Based on Canny Edge Detection. 2016. Department of Computer Science and Communication Engineering,College of Computing and Informatics,Providence University.
[5]H. Zhao, R. Dai, and C. Xiao, "A Machine Vision System for Stacked Substrates Counting With a Robust Stripe Detection Algorithm," IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-10, 2018, doi: 10.1109/TSMC.2017.2766441.
[6]J. G. Daugman, "Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters," JOSA A, vol. 2, no. 7, pp. 1160-1169, 1985.
[7]Y. Lu, "Out-of-focus Blur: Image De-blurring," Michigan State University, 2017, vol. 1710.00620.
[8]A. Pandit and J. Rangole, "Literature review on object counting using image processing techniques," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 3, no. 4, pp. 8509-8512, 2014.
[9]X. Guo and F. Yu, "A Method of Automatic Cell Counting Based on Microscopic Image," presented at the 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, 26-27 Aug. 2013, 2013.
[10]C. R. M. Mauricio, F. K. Schneider, and L. C. d. Santos, "Image-based red cell counting for wild animals blood," presented at the 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 31 Aug.-4 Sept. 2010, 2010.
[11]C. A. Mello, W. P. Dos Santos, M. A. Rodrigues, A. L. B. Candeias, and C. M. Gusmao, "Image segmentation of ovitraps for automatic counting of Aedes aegypti eggs," presented at the 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008.
[12]J. G. A. Barbedo, "A Review on Methods for Automatic Counting of Objects in Digital Images," IEEE Latin America Transactions, vol. 10, no. 5, pp. 2112-2124, 2012, doi: 10.1109/TLA.2012.6362356.
[13]P.-H. Wu and C.-H. Kuo, "A counting algorithm and application of image-based printed circuit boards," Journal of Applied Science and Engineering,Tamkang University, vol. 12, no. 4, pp. 471-479, 2009.
[14]H. Y. Liang, J. S. Wang, W. Li, and L. Y. Sun, Design and Reseach of Paper Counting Algorithem Based on Texture Image. 2017, pp. 416-419.
[15]Z. Gang, Y. Shuo, and C. Xiao, "A Fast Straight-Line Growing Algorithm for Sheet-Counting with Stacked-Paper Images," Berlin, Heidelberg, 2014.
[16]R. C. Gonzalez, & Woods, R. E., Digital image fundamentals, digital imaging processing, 2/E. 2002.
[17]J. Serra, Image Analysis and Mathematical Morphology. Academic Press, Inc., 1983.
[18]A. Kaehler and G. Bradski, Learning OpenCV 3: computer vision in C++ with the OpenCV library. " O'Reilly Media, Inc.", 2016.
[19]D. Forsyth and J. Ponce, Computer Vision: A Modern Approach. (Second edition). Prentice Hall (in English), 2011, p. 792.
[20]A. Sanin, C. Sanderson, and B. C. Lovell, "Shadow Detection: A Survey and Comparative Evaluation of Recent Methods."
[21]W. Huang, K. Y. Kim, Y. Yang, and Y.-S. Kim, "Automatic shadow removal by illuminance in HSV color space," 2015.
[22]R. Hall, Illumination and color in computer generated imagery. Springer-Verlag, 1989, p. 282.
[23]J. F. Hughes, Computer graphics : principles and practice. (in English), 2014.
[24]A. R. Smith, "Color gamut transform pairs," ACM Siggraph Computer Graphics, vol. 12, no. 3, pp. 12-19, 1978.
[25]C. Poynton, "Frequently asked questions about color."
[26]R. Szeliski, Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
[27]A. K. Jain and F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters," Pattern recognition, vol. 24, no. 12, pp. 1167-1186, 1991.
[28]J. R. Movellan, "Tutorial on Gabor filters."
[29]S. E. Grigorescu, N. Petkov, and P. Kruizinga, "Comparison of texture features based on Gabor filters," IEEE Transactions on Image processing, vol. 11, no. 10, pp. 1160-1167, 2002.
[30]N. Petkov and P. Kruizinga, "Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells," Biological cybernetics, vol. 76, no. 2, pp. 83-96, 1997.
[31]N. Otsu, "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66, 1979, doi: 10.1109/TSMC.1979.4310076.
[32]A. K. Jain, Fundamentals of digital image processing. Englewood Cliffs, NJ: Prentice Hall, 1989.
[33]K. Wu, E. Otoo, and K. Suzuki, "Two strategies to speed up connected component labelingalgorithms," Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US), 2005.
[34]C. Grana, D. Borghesani, and R. Cucchiara, "Optimized block-based connected components labeling with decision trees," IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1596-1609, 2010.
[35]S. Lloyd, "Least squares quantization in PCM," IEEE transactions on information theory, vol. 28, no. 2, pp. 129-137, 1982.
[36]D. Arthur and S. Vassilvitskii, "k-means++: The advantages of careful seeding," presented at the Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 2007.
[37]K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, "Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising," IEEE Transactions on Image Processing, vol. 26, no. 7, pp. 3142-3155, 2017.
[38]K. Zhang, W. Zuo, S. Gu, and L. Zhang, "Learning deep CNN denoiser prior for image restoration," presented at the Proceedings of the IEEE conference on computer vision and pattern recognition, 2017.
[39]J. Lehtinen et al., "Noise2noise: Learning image restoration without clean data," ICML, 2018.
[40]S. Guo, Z. Yan, K. Zhang, W. Zuo, and L. Zhang, "Toward convolutional blind denoising of real photographs," presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[41]G. Yu and G. Sapiro, "DCT image denoising: a simple and effective image denoising algorithm," Image Processing On Line, vol. 1, pp. 292-296, 2011.
[42]S. K. Jha and R. Yadava, "Denoising by singular value decomposition and its application to electronic nose data processing," IEEE Sensors Journal, vol. 11, no. 1, pp. 35-44, 2010.
[43]Q. Guo, C. Zhang, Y. Zhang, and H. Liu, "An efficient SVD-based method for image denoising," IEEE transactions on Circuits and Systems for Video Technology, vol. 26, no. 5, pp. 868-880, 2015.
[44]L. Zhang, R. Lukac, X. Wu, and D. Zhang, "PCA-based spatially adaptive denoising of CFA images for single-sensor digital cameras," IEEE transactions on image processing, vol. 18, no. 4, pp. 797-812, 2009.
[45]Y. Zhang, J. Liu, M. Li, and Z. Guo, "Joint image denoising using adaptive principal component analysis and self-similarity," Information Sciences, vol. 259, pp. 128-141, 2014.
[46]S. Wu, W. Lin, L. Jiang, W. Xiong, L. Chen, and S. H. Ong, "An objective out-of-focus blur measurement," presented at the 2005 5th International Conference on Information Communications & Signal Processing, 2005.
[47]V. Karpushin, "Restoration of defocused and blurred images." [Online]. Available: http://yuzhikov.com/articles/BlurredImagesRestoration1.htm
[48]S. M. Kuo, B. H. Lee, and W. Tian, Real-time digital signal processing. Wiley Online Library.
[49]C.-S. Chen, "Introduction to Digital Signal Processing (Discrete-time Signal Processing)."
[50]И. Грузман, В. Киричук, В. Косых, Г. Перетягин, and А. Спектор, Цифровая обработка изображений в информационных системах.
[51]C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images."
[52]S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, "Bilateral filtering: Theory and applications," Foundations and Trends® in Computer Graphics and Vision, vol. 4, no. 1, pp. 1-73, 2009.
[53]V. Aurich and J. Weule, "Non-linear gaussian filters performing edge preserving diffusion," in Mustererkennung 1995: Springer, 1995, pp. 538-545.
[54]S. M. Smith and J. M. Brady, "SUSAN—a new approach to low level image processing," International journal of computer vision, vol. 23, no. 1, pp. 45-78, 1997.
[55]C. C. Wang, "Bilateral recovering of sharp edges on feature-insensitive sampled meshes," IEEE Transactions on Visualization and Computer Graphics, vol. 12, no. 4, pp. 629-639, 2006.
[56]E. P. Bennett and L. McMillan, "Video enhancement using per-pixel virtual exposures," presented at the ACM SIGGRAPH 2005 Papers, Los Angeles, California, 2005.
[57]M. Aleksic, M. Smirnov, and S. Goma, "Novel bilateral filter approach: Image noise reduction with sharpening," presented at the Digital Photography II, 2006.
[58]C. Liu, W. T. Freeman, R. Szeliski, and S. B. Kang, "Noise estimation from a single image," presented at the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), 2006.
[59]G. Jeon, S. Kang, and Y.-S. Lee, "Weight assignment method for interpolation," in Computer Applications for Web, Human Computer Interaction, Signal and Image Processing, and Pattern Recognition: Springer, 2012, pp. 272-278.
[60]F. Durand and J. Dorsey, "Fast bilateral filtering for the display of high-dynamic-range images," presented at the ACM transactions on graphics (TOG), 2002.
[61]K. Inui, S. i. Kaneko, and S. Igarashi, "Robust line fitting using LMedS clustering," Systems and Computers in Japan, vol. 34, no. 14, pp. 92-100, 2003.
[62]P. Meer, D. Mintz, A. Rosenfeld, and D. Y. Kim, "Robust regression methods for computer vision: A review," International journal of computer vision, vol. 6, no. 1, pp. 59-70, 1991.
[63]P. J. Rousseeuw and A. M. Leroy, Robust regression and outlier detection. John wiley & sons, 2005.
[64] F. Meyer, "Color image segmentation," in 1992 International Conference on Image Processing and its Applications, 7-9 April 1992 1992, pp. 303-306.
[65]E. Dubrofsky, "Homography estimation," Diplomová práce. Vancouver: Univerzita Britské Kolumbie, 2009.
[66]D. Kriegman, "Homography estimation."
[67] F. A. Cosio, J. A. M. Flores, M. A. P. Castaneda, S. Solano, and P. Tato, "Automatic counting of immunocytochemically stained cells," in Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 17-21 Sept. 2003 2003, vol. 1, pp. 790-793 Vol.1, doi: 10.1109/IEMBS.2003.1279883.
[68]J. Weston et al., "Towards ai-complete question answering: A set of prerequisite toy tasks," arXiv preprint arXiv:1502.05698, 2015.
[69]J. Kirkpatrick et al., "Overcoming catastrophic forgetting in neural networks," Proceedings of the national academy of sciences, vol. 114, no. 13, pp. 3521-3526, 2017.
[70] S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, "icarl: Incremental classifier and representation learning," in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, 2017, pp. 2001-2010.
[71] F. Zenke, B. Poole, and S. Ganguli, "Continual learning through synaptic intelligence," in Proceedings of the 34th International Conference on Machine Learning-Volume 70, 2017: JMLR. org, pp. 3987-3995.
[72] C. Finn, P. Abbeel, and S. Levine, "Model-agnostic meta-learning for fast adaptation of deep networks," in Proceedings of the 34th International Conference on Machine Learning-Volume 70, 2017: JMLR. org, pp. 1126-1135.
[73]A. Nichol, J. Achiam, and J. Schulman, "On first-order meta-learning algorithms," arXiv preprint arXiv:1803.02999, 2018.