PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Video Assisting System for Garment Manufacturing Technological Flow

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
PL
System z wizualnym wspomaganiem dla kontroli linii technologicznej produkcji odzieży
Języki publikacji
EN
Abstrakty
EN
Although numerous video-inspecting systems have beenimplemented in different manufacturing domains, in the textile industry there is no video assisting/inspecting of assembling operations in the textile/clothes production flow available on the market. There are some particularities of textile and clothes manufacturing units, severely restricting the customisation of existing video assisting systems to the garment industry. Among these, the most important are the irregularity of lighting caused by the partial obstruction of the optic path (flocks and fluffs interposing between the light source – textile detail – video cam) as well as the presence of fabric undulations which can modify the visible shape of the detail to be identified. In order to overcome these limitations, a novel pattern matching algorithm was developed, named the Learn and Match Mini-Pattern. The working principle of this new algorithm will presume the decimation of a pattern in several partial mini patterns.
PL
Jakkolwiek systemy audiowizyjne są stosowane w różnych branżach dla kontroli produkcji, jak dotychczas nie stosowano tego typu systemów dla kontroli produkcji odzieży. W przemyśle odzieżowym istnieją specyficzne warunki różniące produkcję odzieży od wytwarzania innych produktów. Szczególnie istotnym jest nieregularność oświetlenia spowodowana przez częściowe przeszkody ścieżek optycznych (fragmenty materiałów włóknistych pomiędzy źródłem światła a elementami przetwarzanej odzieży) oraz falowanie materiałów, które utrudniają zidentyfikowanie kształtu obrabianego detalu. W celu ominięcia tych trudności opracowano nowy algorytm nazwany „Learn and Match Mini-Pattern”. Algorytm ten pozwala na rozłożenie danego kształtu na szereg elementów cząstkowych.
Rocznik
Strony
135--140
Opis fizyczny
Bibliogr. 43 poz., rys., tab.
Twórcy
autor
  • Faculty of Electrical Engineering, Gheorghe Asachi Technical University of Iaşij, Iasi, Romania
Bibliografia
  • 1. Chen S, Feng J, Zou L. Study of fabric defects detection through Gabor filter based on scale transformation. In: International Conference on Image Analysis and Signal Processing, 2010, pp. 97–99.
  • 2. Zhang YH, Yuen CWM, Wong WK. A new intelligent fabric defect detection and classification system based on Gabor filter and modified Elman neural network. In: 2nd International Conference on Advanced Computer Control, 2010, pp. 652–656.
  • 3. Wang X, Georganas ND, Petriu EM. Automatic woven fabric structure identification by using principal component analysisand fuzzy clustering. In: IEEE Instrumentation and Measurement Technology Conference, 2010, pp. 590–595.
  • 4. Zhang W, Zhao Q, Liao L. Development of a real-time machine vision system for detecting defeats of cord fabrics. In: International Conference on Computer Application and System Modeling, 2010, pp. V12-539–V12-543.
  • 5. Zhang J, Meng X. A Fabric Defect Detection System Based on Image Recognition. In: 2nd International Workshop on Intelligent Systems and Applications, 2010, pp. 1–4.
  • 6. Lien HC, Liu CHA. Method of Inspecting Non-woven Basis Weight Using the Exponential Law of Absorption and Image Processing. Textile Research Journal 2006; 76(7): 547–558.
  • 7. Goswami BM, Datta AK. Detecting Defects in Fabric with Laser-Based Morphological Image Processing. Textile Research Journal 2000; 70(9): 758–762.
  • 8. Shiau YR, Tsai IS, Lin CS. Classifying Web Defects with a Back-Propagation Neural Network by Color Image Processing. Textile Research Journal 2000; 70(7): 633–640.
  • 9. Kuo CFJ, Shih CY, Ho CE, Peng KC. Application of computer vision in the automatic identification and classification of woven fabric weave patterns. Textile Research Journal 2010; 80(20): 2144– 2157.
  • 10. Kim HJ, Kim JS, Lim JH, et al. Detection of Wrapping Defects by a Machine Vision and its Application to Evaluate the Wrapping Quality of the Ring Core Spun Yarn. Textile Research Journal 2009; 79, 17: 1616–1624.
  • 11. Semnani D, Sheikhzadeh M. New Intelligent Method of Evaluating the Regularity of Weft-knitted Fabrics by Computer Vision and Grading Development. Textile Research Journal 2009; 79, 17: 1578–1587.
  • 12. Saeidi RG, Latifi M, Najar SS, et al. Computer Vision-Aided Fabric Inspection System for On-Circular Knitting Machine. Textile Research Journal 2005; 75, 6: 492–497.
  • 13. Jeong SH, Choi HT, Kim SR, et al. Detecting Fabric Defects with Computer Vision and Fuzzy Rule Generation. Part I: Defect Classification by Image Processing. Textile Research Journal 2001; 71, 6: 518–526.
  • 14. Wang XH, Wang JY, Zhang JL, et al. Study on the detection of yarn hairiness morphology based on image processing technique. In: International Conference on Machine Learning and Cybernetics, 2010, pp. 2332–2336.
  • 15. Fabijanska A. A survey of thresholding algorithms on yarn images. In: VIth International Conference on Perspective Technologies and Methods in MEMS Design, 2010, pp. 23–26.
  • 16. Lu Y, Gao W, Liu J. Color matching for colored fiber blends based on the fuzzy c-mean cluster in HSV color space. In: 7th International Conference on Fuzzy Systems and Knowledge Discovery, 2010, pp. 452–455.
  • 17. Ronghua Z, Hongwu C, Xiaoting Z, et al. Unsupervised Color Classification for Yarn-dyed Fabric Based on FCM Algorithm. In: International Conference on Artificial Intelligence and Computational Intelligence, 2010, pp. 497–501.
  • 18. Xu BG, Murrells CM, Tao XM, Automatic Measurement and Recognition of Yarn Snarls by Digital Image and Signal Processing Methods. Textile Research Journal 2008; 78, 5: 439–456.
  • 19. Ikiz Y, Rust JP, Jasper WJ, et al. Fiber Length Measurement by Image Pro- cessing. Textile Research Journal 2001; 71, 10: 905–910.
  • 20. Li X, Li X. Human Body Dimensions Extraction from 3D Scan Data. In: International Conference on Intelligent Computation Technology and Automation, 2010, pp. 441–444.
  • 21. Yu W, Yao M, Xu B. 3-D Surface Reconstruction and Evaluation of Wrinkled Fabrics by Stereo Vision. Textile Research Journal 2009; 79, 1: 36–46.
  • 22 Norton-Wayne L, Mackellar A, Nicklin C. Measurement of garment dimensions using machine vision. In: 3rd International Conference on Image Processing and its Applications, 1989, pp. 197–201.
  • 23. Yin K, Yu W. Image Processing for the Use of Garment Production Detection System. In: Congress on Image and Signal Processing, 2008, pp. 349–352.
  • 24. Cao L, Jiang Y, Jiang M. Automatic measurement of garment dimensions using machine vision. In: International Conference on Computer Application and System Modeling, 2010, pp. V9- 30–V9-33.
  • 25. Makita S, Kadono Y, Maeda Y, et al. Manipulation of submillimeter-sized electronic parts using force control and vision-based position control. In: International Conference on Intelligent Robots and Systems, 2007, pp. 1834–1839.
  • 26. Zhao H, Cheng J, Jin J. NI vision based automatic optical inspection (AOI) for surface mount devices: Devices and method. In: International Conference on Applied Superconductivity and Electromagnetic Devices, 2009, pp. 356–360.
  • 27. Lu S, Zhang X, Kuang Y. An Integrated Inspection Method based on Machine Vision for Solder Paste Depositing. In: IEEE International Conference on Control and Automation, 2007, pp. 137–141.
  • 28. Wu H, Feng G, Li H, et al. Automated visual inspection of surface mounted chip components. In: International Conference on Mechatronics and Automation, 2010, pp. 1789–1794.
  • 29. Yang M, Castellani M, Landot R, et al. Automated optical inspection method for MEMS fabrication. In: International Conference on Mechatronics and Automation, 2010, pp. 1923–1931.
  • 30. Xiang X, He J, Yang S. Pinhole defects detection of aluminum foil based on machine vision. In: 9th International Conference on Electronic Measurement & Instruments, 2009, pp. 2-38–2-41.
  • 31. Liu YJ, Kong JY, Wang XD, et al. Research on image acquisition of automatic surface vision inspection systems for steel sheet. In: 3rd International Conference on Advanced Computer Theory and Engineering, 2010, pp. V6-189–V6-192.
  • 32. Adamo F, Attivissimo F, Di Nisio A, et al. An online defects inspection system for satin glass based on machine vision. In: IEEE Instrumentation and Measurement Technology Conference, 2009, pp. 288–293.
  • 33. Muramatsu S, Otsuka Y, Takenaga H, et al. Image processing device for automotive vision systems. In: IEEE Intelligent Vehicle Symposium, 2002, pp. 121–126.
  • 34. Tsai YM, Tsai CC, Huang KY, et al. An intelligent vision-based vehicle detection and tracking system for automotive applications. In: IEEE International Conference on Consumer Electronics, 2011, pp. 113–114.
  • 35. Ambrosch K, Zinner C, Leopold H. A miniature embedded stereo vision system for automotive applications. In: IEEE 26th Convention of Electrical and Electronics Engineers in Israel, 2010, pp. 786–789.
  • 36. Runtz KJ. Electronic recognition of plant species for machine vision sprayer control systems. In: WESCANEX ‚91 IEEE Western Canada Conference on Computer, Power and Communications Systems in a Rural Environment, 1991, pp. 84–88.
  • 37. Moonrinta J, Chaivivatrakul S, Dailey MN, et al. Fruit detection, tracking, and 3D reconstruction for crop mapping and yield estimation. In: 11th International Conference on Control Automation Robotics & Vision, 2010, pp. 1181–1186.
  • 38. Suapang P, Dejhan K, Yimmun S. Medical image processing and analysis for nuclear medicine diagnosis. In: International Conference on Control Automation and Systems, 2010, pp. 2448–2451.
  • 39. Oprea S, Lita I, Jurianu M, et al. Digital image processing applied in drugs industry for detection of broken aspirin tablets. In: 31st International Spring Seminar on Electronics Technology, 2008, pp. 121–124.
  • 40. Acton S. Biomedical Image Analysis at the Cellular Level. In: International Machine Vision and Image Processing Conference, 2008, pp. 27–27.
  • 41. Kanade T, Yin Z, Bise R, et al. Cell image analysis: Algorithms, system and applications. In: IEEE Workshop on Applications of Computer Vision, 2011, pp. 374–381.
  • 42. Mayo P, Ródenas F, Verdú G, et al. Analysis of image quality parameter of conventional and dental radiographic digital images. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, pp. 3174–3177.
  • 43. Mythili A, Christopher JJ, Ramakrishnan S. Estimation of Compressive Strength of Femur Bones using Radiographic Imaging and Spectral Analysis. In: International Conference on Communications and Networking, 2008, pp. 392–395.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-73822ceb-2be5-43e4-b393-ac5696a17a56
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.