https://oldena.lpnu.ua/handle/ntb/52447| Title: | Analysis of Metal Defects by Clustering the Sample and Distributed Cumulative Histogram |
| Authors: | Melnyk, Roman Kalychak, Yurii |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | Melnyk R. Analysis of Metal Defects by Clustering the Sample and Distributed Cumulative Histogram / Roman Melnyk, Yurii Kalychak // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 563–567. — (Machine Vision and Pattern Recognition). |
| Bibliographic description (International): | Melnyk R. Analysis of Metal Defects by Clustering the Sample and Distributed Cumulative Histogram / Roman Melnyk, Yurii Kalychak // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 563–567. — (Machine Vision and Pattern Recognition). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | image intensity surface defects clustering pixel segmentation inversion distributed cumulative histogram |
| Number of pages: | 5 |
| Page range: | 563-567 |
| Start page: | 563 |
| End page: | 567 |
| Abstract: | In this paper the clustering algorithm was used to classify the regions of the metal sample with defects to determine their coordinates. The informative distributed cumulative histogram is proposed. To measure sizes and intensity of defects the IDCH image is transformed and clustered. |
| URI: | https://ena.lpnu.ua/handle/ntb/52447 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://www.weco.com/surface-inspection http://cilabs.kaist.ac.kr/research/image-analysis/defect-detection |
| References (Ukraine): | [1] L.J. Wells, M. S. Shafae and J.A. Camelio, “Automated Surface Defect Detection Using High-Density Data,” J. Manuf. Sci. Eng., 138(7), Mar, 2016. [2] I. Ahn and Ch. Kim, "Finding Defects in Regular-Texture Images," 16th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Hiroshima, Japan, pp. 478-480, Feb. 2010. [3] J. Choi and Ch. Kim, "Unsupervised Detection of Surface Defects: A Two-Step Approach," IEEE International Conference of Image Processing (ICIP), Orlando, USA, pp. 1037-1040, Sep. 2012. [4] L.A.O. Martins, F.L.C. Padua, and P.E.M. Almeida, “Automatic Detection of Surface Defects on Rolled Steel Using Computer Vision and Artificial Neural Networks IECON,” 36th Annual Conference on IEEE Industrial Electronics Society, pp. 1081-1086, 2010. [5] S. Jahanbina, A.C. Bovika, E. Perezb, and D. Nair, “Automatic Inspection of Textured Surfaces by Support Vector Machines,” [Electronic resource] Link: ttps://live.ece.utexas.edu/publications [6] Wintriss defects gallery [Electronic resource] Link: http://www.weco.com/surface-inspection [7] Defect detection of various films [Electronic resource] Link: http://cilabs.kaist.ac.kr/research/image-analysis/defect-detection [8] V.H. Pham, and B.R. Lee, “An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm,” Vietnam Journal of Computer Science, vol. 2, iss. 1, pp. 25–33, February 2015, [9] K. Zheng, Y.-S. Chang, K.-H. Wang, and Y. Yao, “Thermographic clustering analysis for defect detection in CFRP structures,” Polymer Testing, vol. 49, pp. 73-81, February 2016. [10] R. Xu, and D. Wunsch, “Survey of clustering algorithms”, IEEE Transactions on Neural Networks, vol. 16, iss. 3, pp. 645 – 678, May 2005. [11] S. Naz, H. Majeed, and H. Irshad, “Image segmentation using fuzzy clustering: A survey,” 6th International Conference on Emerging Technologies (ICET), pp. 181 – 186, 18-19 Oct. 2010. [12] S. Thilagamani1 and N. Shanthi, “A Survey on Image Segmentation Through Clustering,” International Journal of Research and Reviews in Information Sciences, vol. 1, no. 1, pp. 14-17, March 2011. [13] Y. Yang, D. Xu, F.Nie, S. Yan, and Y. Zhuang, “Clustering Using Local Discriminant Models and Global Integration,” IEEE Transactions on Image Processing, vol. 19, iss. 10, pp. 2761 – 2773, Oct. 2010 |
| References (International): | [1] L.J. Wells, M. S. Shafae and J.A. Camelio, "Automated Surface Defect Detection Using High-Density Data," J. Manuf. Sci. Eng., 138(7), Mar, 2016. [2] I. Ahn and Ch. Kim, "Finding Defects in Regular-Texture Images," 16th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Hiroshima, Japan, pp. 478-480, Feb. 2010. [3] J. Choi and Ch. Kim, "Unsupervised Detection of Surface Defects: A Two-Step Approach," IEEE International Conference of Image Processing (ICIP), Orlando, USA, pp. 1037-1040, Sep. 2012. [4] L.A.O. Martins, F.L.C. Padua, and P.E.M. Almeida, "Automatic Detection of Surface Defects on Rolled Steel Using Computer Vision and Artificial Neural Networks IECON," 36th Annual Conference on IEEE Industrial Electronics Society, pp. 1081-1086, 2010. [5] S. Jahanbina, A.C. Bovika, E. Perezb, and D. Nair, "Automatic Inspection of Textured Surfaces by Support Vector Machines," [Electronic resource] Link: ttps://live.ece.utexas.edu/publications [6] Wintriss defects gallery [Electronic resource] Link: http://www.weco.com/surface-inspection [7] Defect detection of various films [Electronic resource] Link: http://cilabs.kaist.ac.kr/research/image-analysis/defect-detection [8] V.H. Pham, and B.R. Lee, "An image segmentation approach for fruit defect detection using k-means clustering and graph-based algorithm," Vietnam Journal of Computer Science, vol. 2, iss. 1, pp. 25–33, February 2015, [9] K. Zheng, Y.-S. Chang, K.-H. Wang, and Y. Yao, "Thermographic clustering analysis for defect detection in CFRP structures," Polymer Testing, vol. 49, pp. 73-81, February 2016. [10] R. Xu, and D. Wunsch, "Survey of clustering algorithms", IEEE Transactions on Neural Networks, vol. 16, iss. 3, pp. 645 – 678, May 2005. [11] S. Naz, H. Majeed, and H. Irshad, "Image segmentation using fuzzy clustering: A survey," 6th International Conference on Emerging Technologies (ICET), pp. 181 – 186, 18-19 Oct. 2010. [12] S. Thilagamani1 and N. Shanthi, "A Survey on Image Segmentation Through Clustering," International Journal of Research and Reviews in Information Sciences, vol. 1, no. 1, pp. 14-17, March 2011. [13] Y. Yang, D. Xu, F.Nie, S. Yan, and Y. Zhuang, "Clustering Using Local Discriminant Models and Global Integration," IEEE Transactions on Image Processing, vol. 19, iss. 10, pp. 2761 – 2773, Oct. 2010 |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Melnyk_R-Analysis_of_Metal_Defects_563-567.pdf | 320.48 kB | Adobe PDF | View/Open | |
| 2018_Melnyk_R-Analysis_of_Metal_Defects_563-567__COVER.png | 1.42 MB | image/png | View/Open |
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