Journal of Information Processing Systems

  • 제8권2호
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  • Pages.359-374
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  • 2012
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Automatic Detection of Texture-defects using Texture-periodicity and Jensen-Shannon Divergence

  • Asha, V. (Dept. of Computer Applications, New Horizon College of Engineering, India & JSS Research Foundation, SJCE Campus, University of Mysore) ;
  • Bhajantri, N.U. (Dept. of Computer Science and Engineering, Govt. Engineering College) ;
  • Nagabhushan, P. (Dept. of Studies in Computer Science, University of Mysore)
  • 투고 : 2011.09.02
  • 심사 : 2012.01.02
  • 발행 : 2012.06.30
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초록

In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the Jensen-Shannon Divergence, which is a symmetrized and smoothed version of the Kullback-Leibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks. Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention.

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참고문헌

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피인용 문헌

  1. Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection 2017, https://doi.org/10.1007/s00500-017-2709-1
  2. Defect detection on patterned fabrics using texture periodicity and the coordinated clusters representation vol.87, pp.15, 2017, https://doi.org/10.1177/0040517516660885