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A Simplified pulse-coupled neural network for adaptive segmentation of fabric defects

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Abstract

This paper describes an adaptive image-segmentation method based on a simplified pulse-coupled neural network (PCNN) for detecting fabric defects. Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors (e.g., illumination, noise). A new parameter called the deviation of the contrast (DOC) was introduced to describe the contrast difference in row and column between the analyzed image and a defect-free image of the same fabric. The DOC essentially weakens the influence of the weave texture and the illumination. The simplification of PCNN reduces the number of the network’s parameters by utilizing the local and global DOC information for the parameter selections. The validation tests on the developed algorithms were performed with fabric images captured by a line-scan camera on an inspection machine, and with images from TILDA’s Textile Texture Database (http://lmb.informatik.uni-freiburg.de/research/dfg-texture/tilda) as well.

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Authors and Affiliations

  1. School of Computer Science, Xi’an Polytechnic University, Xi’an, 710048, People’s Republic of China

    Meihong Shi, Shoushan Jiang & Huiran Wang

  2. Department of Human Ecology, University of Texas at Austin, Austin, TX, 78712, USA

    Bugao Xu

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  1. Meihong Shi
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  2. Shoushan Jiang
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  3. Huiran Wang
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  4. Bugao Xu
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Correspondence to Bugao Xu.

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Shi, M., Jiang, S., Wang, H. et al. A Simplified pulse-coupled neural network for adaptive segmentation of fabric defects. Machine Vision and Applications 20, 131–138 (2009). https://doi.org/10.1007/s00138-007-0113-z

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  • DOI: https://doi.org/10.1007/s00138-007-0113-z

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