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山东大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (4): 1-8.doi: 10.6040/j.issn.1672-3961.2.2013.344

• 机器学习与数据挖掘 •    下一篇

基于纹理差异视觉显著性的织物疵点检测算法

李春雷1, 张兆翔2, 刘洲峰1, 廖亮1, 赵全军1   

  1. 1. 中原工学院电子信息学院, 河南 郑州 450007;
    2. 北京航空航天大学计算机学院智能识别与图像处理实验室, 北京 100191
  • 收稿日期:2013-04-30 修回日期:2014-06-26 出版日期:2014-08-20 发布日期:2013-04-30
  • 作者简介:李春雷（1979-），男，河南商水人，副教授，博士，主要研究方向为织物图像处理.E-mail：lichunlei1979@gmail.com
  • 基金资助:
    国家自然科学基金资助项目（61202499，61379113）；河南省基础与前沿技术研究计划资助项目（132300410163，142300410042）

A novel fabric defect detection algorithm based on textural differential visual saliency model

LI Chunlei1, ZHANG Zhaoxiang2, LIU Zhoufeng1, LIAO Liang1, ZHAO Quanjun1   

  1. 1. School of Electronic and Information Engineering, Zhongyuan University of Technology, Zhengzhou 450007, Henan, China;
    2. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
  • Received:2013-04-30 Revised:2014-06-26 Online:2014-08-20 Published:2013-04-30

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摘要： 由于织物图像纹理多样化及疵点类别较多，为了更有效地检测织物疵点，结合织物图像特性及借鉴人类视觉感知机理，提出一种基于纹理差异视觉显著性模型的织物疵点检测算法。该算法首先对图像进行分块，计算各个图像块LBP（local binary pattern）纹理特征，与图像块平均纹理特征的相似度比较，进行显著度计算，从而有效突出了疵点区域。最后利用改进阈值分割算法，实现对疵点区域的定位。通过与已有视觉显著性模型进行比较，得出该算法更能有效地突出疵点区域；同时，分割结果与已有织物疵点检测算法相比发现，该算法具有更强的疵点检测及定位能力。

关键词: 疵点检测, 织物疵点, 局部二值模式, 纹理差异, 分割, 视觉显著性

Abstract: In order to effectively detect defect for fabirc image with variety of defects and complex texture, a novel fabric defect detection scheme based on textural difference-based visual saliency model was proposed, which considered the characteristics of fabric image and human visual perception. First, the test image was split into image blocks, and textural feature was extracted using LBP operator for each image block. Second, saliency was calculated by comparing their textural feature with the average texture feature. Finally, the threshold segmentation algorithm was used to localize the defect region. Comparing with the current saliency model, the proposed saliency model could effectively distinguish the defect. In addition, segmentation scheme was superior to the current defect detection algorithm in detection and localization.

Key words: fabric defect, defect detection, textural difference, segment, visual saliency, local binary pattern

中图分类号: 

  • TP391
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