دانشگاه تربیت دبیر شهید رجائی

  اخبار و اعلانات

 آمار

تعداد نشریات13
تعداد شماره‌ها200
تعداد مقالات2,008
تعداد مشاهده مقاله2,679,471
تعداد دریافت فایل اصل مقاله1,936,870
Ravendran, Ahalya, Rianmora, Suchada. (1400). Application of image-based acquisition techniques for additive manufacturing using canny edge detection. فناوری آموزش, 10(2), 391-404. doi: 10.22061/jcarme.2019.4355.1524
Ahalya Ravendran; Suchada Rianmora. "Application of image-based acquisition techniques for additive manufacturing using canny edge detection". فناوری آموزش, 10, 2, 1400, 391-404. doi: 10.22061/jcarme.2019.4355.1524
Ravendran, Ahalya, Rianmora, Suchada. (1400). 'Application of image-based acquisition techniques for additive manufacturing using canny edge detection', فناوری آموزش, 10(2), pp. 391-404. doi: 10.22061/jcarme.2019.4355.1524
Ravendran, Ahalya, Rianmora, Suchada. Application of image-based acquisition techniques for additive manufacturing using canny edge detection. فناوری آموزش, 1400; 10(2): 391-404. doi: 10.22061/jcarme.2019.4355.1524

Application of image-based acquisition techniques for additive manufacturing using canny edge detection

Journal of Computational & Applied Research in Mechanical Engineering (JCARME)
مقاله 9، دوره 10، شماره 2 - شماره پیاپی 20، تیر 2021، صفحه 391-404    اصل مقاله (577.74 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22061/jcarme.2019.4355.1524
نویسندگان
Ahalya Ravendran* ؛ Suchada Rianmora
School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology, Thammasat University, Pathumthani, Thailand.
تاریخ دریافت: 16 آبان 1397،  تاریخ بازنگری: 31 اردیبهشت 1398،  تاریخ پذیرش: 19 خرداد 1398 
چکیده
Edge is an indispensable characteristic of an image, defined as the contour between two regions with significant variance in terms of surface reflectance, illumination, intensity, color, and texture. Detection of edges is a basic requirement for diverse contexts for design automation. This study presents a guideline to assign appropriate threshold and sigma values for the Canny edge detector to increase the efficiency of additive manufacturing. The algorithm uses different combinations of threshold and sigma on a color palette, and the results are statistically formulated using multiple regression analysis with an accuracy of 95.93%. An image-based acquisition technique system is designed and developed for test applications to create three-dimensional objects. In addition, a graphical user interface is developed to convert a selected design of a complex image to a three-dimensional object with the generation of Cartesian coordinates of the detected edges and extrusion. The developed system reduces the cost and time of developing an existing design of an object for additive manufacturing by 20% and 70%, respectively.
کلیدواژه‌ها
Edge detection؛ Additive manufacturing؛ Product design؛ Image-based acquisition؛ Cartesian-coordinates؛ Industrial automation
مراجع
[1]  W. J. Hughes, Joint Federal Aviation Administration-Air Force Workshop on Qualification/ Certification of Additively Manufactured Parts. [online] Summary Report, Federal Aviation Administration, (2016). 

[2] Norsk Titanium AS. ‘Norsk Titanium to Deliver the World’s First FAA-Approved, 3D-Printed, Structural Titanium Components to Boeing’ Business Wire, (2017). 

[3]  John Canny, “A Computational Approach to Edge Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-8, No. 6, pp. 679 – 698, (1986).

[4]  Irwin Sobel, Camera Models and Machine Perception. Doctoral Dissertation, Stanford University, Stanford,CA, United States of America, (1970).

[5]  Lawrence G. Roberts, “Machine Perception of Three-Dimensional Solids”, Doctoral Dissertation, M Massachusetts Institute of Technology, United States of America, (1963).

[6]  Guner S. Robinson, “Detection and coding of edges using directional masks”, in Proceedings of the Seminar in advances in image transmission techniques, San Diego, California, pp. 117-125, (1976).

[7]  D. C. Marr and Ellen Hildreth, “Theory of Edge Detection”, in Proceedings of the Royal Society of London, Vol. 207, No. 1167, (1980).

[8] J. J. M. S. Prewitt, “Object enhancement and extraction”, Picture Proc. and Psychopictorics, New York: Academic Press. pp. 75-149, (1970).

[9]  E. R. Davies, “Chapter 5 – Edge detection”, Computer Vision - Principles, Algorithms, Applications, Learning, 5th ed., United Kingdom, (2018).

[10] Poonam Dhankhar and Neha Sahu, “A Review and Research of Edge Detection Techniques for Image Segmentation”, International Journal of Computer Science and Mobile Computing, Vol. 2, No. 7, pp. 86-92, (2013).

[11] Raman Maini, and Himanshu Aggarwal, “Study and Comparison of Various Image Edge Detection Techniques”, International Journal of Image Processing, Vol. 3, No. 1, pp. 1-11, (2009).

[12]  B. O Sadiq, S. M. Sani and S. Garba, “Edge Detection: A Collection of Pixel based Approach for Colored Images”, International Journal of Computer Applications, Vol. 113, No. 5, pp. 29-32, (2015).

[13] Djemel Ziou, and Salvatore Tabbone, “Edge Detection Techniques - An Overview”, International Journal of Pattern Recognition and Image Analysis, Vol. 8, pp.537-559, (1998).

[14] R. C. Gonzalez and R. E. Woods. Digital Image Processing, 2nd ed., Beijing: Publishing House of Electronics Industry, (2007).

[15]  John Canny, “Improved Algorithms for Sign Determination and Existential Quantifier Elimination”, The Computer Journal, Vol. 36, No. 5, pp. 109-418, (1993).

[16] Zhan Chaohui, Duan Xiaohui, Xu Shuoyu, Song Zheng and Luo Min, “An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection”, Fourth International Conference on Image and Graphics, (2007).

[17] Biman Debbarma and Dibyendu Ghoshal, “A Modified Canny Edge Detection Algorithm with Variable Sigma”, International Journal of Emerging Trends in Electrical and Electronics, Vol. 1, No. 2, (2013).

[18] Cai-Xia Deng, Gui-Bin Wang and Xin-Rui Yang, “Image Edge Detection Algorithm Based on Improved Canny Operator”, In. Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition. pp. 168-172, (2013).

[19] Yingke Feng, Jinmin Zhang, and Siming Wang, “A new edge detection algorithm based on Canny idea”, 2nd International Conference on Materials Science, Resource and Environmental Engineering, (2017).

[20]  Yuan-Kai Huo, Gen Wei, Yu-Dong  Zhang and Le-Nan Wu, “An Adaptive Threshold for the Canny Operator of Edge Detection”, International Conference on Image Analysis and Signal Processing, pp. 371-374, (2010).

[21] Firas A. Jassim, “Semi-Optimal Edge Detector based on Simple Standard Deviation with Adjusted Thresholding”, International Journal of Computer Applications, Vol. 68, No. 2, April, (2013).

[22] Gao Jie and Liu Ning, “An improved adaptive threshold canny edge detection algorithm”, International Conference on Computer Science and Electronics Engineering, pp. 164-168, (2012).

[23] Weibin Rong, Zhanjing Li, Wei Zhang and Lining Sun, “An Improved Canny Edge Detection Algorithm”, IEEE Proceedings on International Conference on Mechatronics and Automation, pp. 577-582, (2014).

[24]  Xun Wang, and Jian-Qiu Jin, “An Edge Detection Algorithm Based on Improved CANNY Operator”, Seventh International Conference on Intelligent Systems Design and Applications, (2007).

[25] Huili Zhao, Guofeng Qin and Xingjian Wang, “Improvement of Canny Algorithm Based on Pavement Edge Detection”, Third International Congress on Image and Signal Processing, (2010).

[26] Nobuyuki Otsu, “A Threshold Selection Method from Gray-Level Histograms”, IEEE Transactions on Systems, Man and Cybernetics. Vol. 1, (1979).

[27]  Suheir M. El Bayoumi Harb, Nor Ashidi Mat Isa and Samy A. Salamah, “Improved image magnification algorithm based on Otsu thresholding”, Computers and Electrical Engineering, Vol. 46, pp. 338–355, (2015).

[28] Mengxing Huang, Wenjiao Yu and Donghai Zhu, “An Improved Image Segmentation Algorithm Based on the Otsu Method”, 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 135-139, (2012).

[29] Jin Li-Sheng, Tian Lei, Wang Rong-ben, Guo Lie and Chu Jiang-wei,  “An Improved Otsu Image Segmentation Algorithm for Path Mark Detection under Variable Illumination”, IEEE Proceedings on Intelligent Vehicles Symposium, pp. 840-844, (2005).

[30] Zhong Qu, and Li Zhang “Research on Image segmentation Based on the Improved Otsu Algorithm”, Second International Conference on Intelligent Human-Machine Systems and Cybernetics, (2010).

[31] Chunshi Sha, Jian Hou and Hongxia Cui, “A robust 2D Otsu’s thresholding method in image segmentation”, Journal of Visual Communication and Image Representation, Vol. 41, pp. 339–351, (2016).

[32] Xiangyang Xu, Shengzhou Xu, Lianghai Jin and Enimn Song, “Characteristic analysis of Otsu threshold and its applications”, Pattern Recognition Letters, Vol. 32 pp. 956–961, (2011).

[33]  J. A. Yague-Fabra, S. Ontiveros, R. Jimenez, S. Chitchian, G. Tosello and  S. Carmignato, “A 3D edge detection technique for surface extraction in computed tomography for dimensional metrology applications”, CIRP Annals - Manufacturing Technology, Vol. 62, pp. 531-534, (2013).

[34] Saeed Hosseinzadeh Hanzaei, Ahmad  Afshar and Farshad Barazandeh, “Automatic detection and classification of the ceramic tiles’ surface defects”, Pattern Recognition, Vol. 66, (2017).

[35] Zeljko Hocenski, Suzana Vasilic, and Verica Hocenski, “Improved Canny Edge Detector in Ceramic Tiles Defect Detection”, 32nd Annual Conference on IEEE Industrial Electronics, (2006).

[36] Mayur A. Inglea and Girish R. Talmaleb, “Respiratory Mask Selection and Leakage Detection System Based on Canny Edge Detection Operator”, Procedia Computer Science, Vol. 78, pp. 323-329, (2016).

[37] Jie Lei, Xin Gao, Zunlei Feng, Huamou Qiu and Mingli Song, “Scale Insensitive and Focus Driven Mobile Screen Defect Detection in Industry”, Neurocomputing, Vol. 294, pp. 72-81, (2018).

[38] Fen Zhanga, Naosheng Qiao and Jianfeng Li, “A PCB photoelectric image edge information detection method”, Optik, Vol. 144. pp. 642-646, (2017).

[39] P. Srinivas, Y. L. Malathilatha and M. V. N. K. Prasad, “Image Processing Edge Detection Technique used for Traffic Control Problem”, International Journal of Computer Science and Information Technologies, Vol. 4, pp 17 – 20, (2013).

[40] Heiner Lasi, Peter Fettke, Hans-Georg Kemper, Thomas Feld and Michael Hoffmann, “Industry 4.0”, Business and Information Systems Engineering, Vol.6, pp. 239-242, (2014).

[41] Matthew Lanaro, David P. Forrestal, Stefan Scheurer, Damien J. Slinger, Sam Liao, Sean K. Powell and Maria A. Woodruff, “3D printing complex chocolate objects: Platform design, optimization and evaluation”, Journal of Food Engineering, (2017).

[42] Zhenbin Liu, Min Zhang, Bhesh Bhandari and Yuchuan Wang, “3D printing: Printing precision and application in food sector”, Trends in Food Science & Technology, (2017).

[43] M. Van Eijnatten, R. Dijk, J.  Dobbe, G. Streekstra, J. Koivisto and J. Wolff “CT image segmentation methods for bone used in medical additive manufacturing”, Medical Engineering and Physics, Vol. 51, pp. 6-16, (2018).

[44] Javeed Shaikh Mohammed, “Applications of 3D printing technologies in oceanography”, Methods in Oceanography, Vol. 17, pp. 97-117, (2016).

[45] Roland K. Chen, Yu-an Jin, Jeffrey Wensman and Albert Shih, “Additive manufacturing of custom orthoses and prostheses—A review”, Additive Manufacturing, Vol. 12, pp. 77-89, (2016).

[46] H.K. Chan, J. Griffin, J. J. Lim, F. Zeng and A. S. F. Chiu, “The impact of 3D Printing Technology on the supply chain: Manufacturing and legal perspectives”, International Journal of Production Economics, Vol. 205, pp. 156-162, (2018).

[47] Ben Ezair, Saul Fuhrmann and Gershon Elber, “Volumetric covering print-paths for additive manufacturing of 3D models”, Computer-Aided Design, Vol. 100, pp. 1-13, (2018).

[48] Sambit Ghadai, Aditya Balu, Soumik Sarkar and  Adarsh Krishnamurthy “Learning localized features in 3D CAD models for manufacturability analysis of drilled holes”, Computer Aided Geometric Design, Vol. 62, pp. 263-275, (2018).

[49] R. A. Katkar, R. M. Taft and G. T. Grant, “3D Volume Rendering and 3D Printing (Additive Manufacturing”, Dental Clinics of North America, Vol. 62, pp. 393-402, (2018).

[50] Tuan D. Ngo, Alireza Kashani,  Gabriele Imbalzano, Kate T. Q. Nguyen and David Hui, “Additive manufacturing (3D printing): A review of materials, methods, applications and challenges”, Composites Part B: Engineering, Vol. 143, pp. 172-196, (2018).

[51] Nelson Vargas-Alfredo, Helmut Reinecke, Alberto Gallardo, Adolfo O. Campo and Juan Rodriguez- Hernandez, “Fabrication of 3D printed objects with controlled surface chemistry and topography”, European Polymer Journal, Vol. 98, pp. 21-27, (2018).

[52] Xiao Li, Min Fang, Ju-Jie Zhang and Jinqiao Wu, “Domain adaptation from RGB-D to RGB images”, Signal Processing, Vol. 131, pp. 27-35, (2017).

[53] D. Jeyabharathi and D. Dejey, “Vehicle Tracking and Speed Measurement system (VTSM) based on novel feature descriptor: Diagonal Hexadecimal Pattern (DHP)”, Journal of Visual Communication and Image Representation, Vol. 40, pp. 816-830, (2016).

[54] D. Jeyabharathi and D. Dejey, “New feature descriptor: Extended Symmetrical-Diagonal Hexadecimal Pattern for efficient background subtraction and object tracking”, Computers and Electrical Engineering, Vol. 66, pp. 454-473, (2018).

[55] Xingyuan Wang, Xiaoqiang Zhu, Xiangjun Wu and Yingqian Zhang, “Image encryption algorithm based on multiple mixed hash functions and cyclic shift”, Optics and Lasers in Engineering, Vol. 107, pp. 370-379, (2018).

[56] A. G. More and S. K. Gupta, “Predictive modelling of chromium removal using multiple linear and nonlinear regression with special emphasis on operating parameters of bio electrochemical reactor”, Journal of Bioscience and Bioengineering, Vol. 126, pp. 205-212, (2018).

[57] Omid Doosti-Irani, Mahmood Reza  Golzarian, Mohammad Hossein Aghkhani, Hasan Sadrnia and Mahboobe Doosti-Irani, “Development of multiple regression model to estimate the apple’s bruise depth using thermal maps”, Postharvest Biology and Technology, Vol. 116, pp. 75-79, (2016).

[58] Richard Kicsiny, “Multiple linear regression based model for solar collectors”, Solar Energy, Vol. 110, pp. 496-506, (2014).

[59] Richard Kicsiny, “Improved multiple linear regression based models for solar collectors”, Renewable Energy, Vol. 91, pp. 224-232, (2016).

[60] Richard Kicsiny, ‘Black-box model for solar storage tanks based on multiple linear regression’, Renewable Energy, Vol. 125, pp. 857-865, (2018).

[61] Tianhong Liu, Haikun Wei and Kanjian Zhang, “Wind power prediction with missing data using Gaussian process regression and multiple imputation”, Applied Soft Computing, Vol. 71, pp. 905-916, (2018).

[62] Mohammad Reza Moghaddasi and Majid Noorian-Bidgoli, “ICA-ANN, ANN and multiple regression models for prediction of surface settlement caused by tunneling”, Tunnelling and Underground Space Technology, Vol. 79, pp. 197-209, (2018).

آمار
تعداد مشاهده مقاله: 1,062
تعداد دریافت فایل اصل مقاله: 451


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب