J4 ›› 2011, Vol. 38 ›› Issue (5): 65-72.doi: 10.3969/j.issn.1001-2400.2011.05.011

• 研究论文 • 上一篇    下一篇



  1. (西安电子科技大学 综合业务网理论及关键技术国家重点实验室,陕西 西安  710071)
  • 收稿日期:2011-01-19 出版日期:2011-10-20 发布日期:2012-01-14
  • 通讯作者: 崔玲玲
  • 作者简介:崔玲玲(1982-),女,西安电子科技大学博士研究生,E-mail: llcuisx@gmail.com
  • 基金资助:


Novel algorithm for automated detection of fabric defect images

CUI Lingling;LU Zhaoyang;LI Jing;LI Yihong   

  1. (State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an  710071, China)
  • Received:2011-01-19 Online:2011-10-20 Published:2012-01-14
  • Contact: CUI Lingling


针对布匹图像非下采样Contourlet分解系数能更好地描述瑕疵图像的轮廓特性,同时具有平移不变性和多方向性等优点,提出一种新的瑕疵自动检测算法.该算法通过非下采样Contourlet变换得到图像的多尺度、多方向稀疏表示|在此基础上,通过代价函数选择最优子带,得到较鲁棒性的描述| 最后实时地估计瑕疵和非瑕疵图像的混合高斯模型参数,有效地避免了对每一类瑕疵的估计,显著地降低了计算量.实验结果表明,与现有经典算法相比,该算法的主观效果和客观评价性能都有明显改进.

关键词: 瑕疵检测, 非下采样Contourlet变换, 混合高斯模型, 小波变换


Considering the advantages that decomposition coefficients in the non subsampled Contourlet of fabric images can describe the contour characteristics in a better way, and that they have shift-invariant and multidirection, a novel algorithm for automated detection of fabric defect images is presented. Firstly, the nonsubsampled Contourlet transform (NSCT) is used to perform the sparse representations in multi-scales and multi-directions. On this basis, the optimal sub-bands of NSCT are selected by the cost function, and then the robust descriptions are obtained. Finally, the parameters of defect and nondefect images are timely estimated separately by the Mixture Gaussian Model(MGM), which effectively avoids estimating each defect and reduces the computational complexity evidently. Experimental results show that the proposed algorithm can lead to a better performance than the traditional algorithms in subjective effects and objective evaluation.

Key words: defect detection, nonsubsampled Contourlet transform(NSCT), mixture gaussian model(MGM), wavelet transforms


  • TN911.73