...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Fibres & textiles in Eastern Europe >Fabric Defect Detection Using a Hybrid and Complementary Fractal Feature Vector and FCM-based Novelty Detector
【24h】

Fabric Defect Detection Using a Hybrid and Complementary Fractal Feature Vector and FCM-based Novelty Detector

机译：使用混合和互补的分形特征向量和基于FCM的新颖性检测器检测织物缺陷

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automated detect detection in woven fabrics for quality control is still a challenging novelty detection problem. This work presents five novel fractal features based on the box-counting dimension to address the novelty detection of fabric defect. Making use of the formation of woven fabric, the fractal features are extracted in a one-dimension series obtained by projecting a fabric image along the warp and weft directions, where their complementarity in discriminating defects is taken into account. Furthermore a new novelty detector based on fuzzy c-means (FCM) is devised to deal with one-class classification of the features extracted. Finally, by jointly applying the features proposed and the FCM based novelty detector, we evaluate the method proposed for eight datasets with different defects and textures, where satisfying results are achieved with a low overall missing detection rate.
机译：机织织物中用于质量控制的自动检测仍然是一个具有挑战性的新颖性检测问题。这项工作提出了基于盒计数维度的五个新颖的分形特征，以解决织物缺陷的新颖性检测。利用机织织物的形成，以一维序列的形式提取分形特征，该维数是通过将织物图像沿经向和纬向投影而获得的，其中考虑了它们在鉴别缺陷方面的互补性。此外，设计了一种新的基于模糊c均值（FCM）的新颖性检测器来处理提取的特征的一类分类。最后，通过结合所提出的特征和基于FCM的新颖性检测器，我们针对具有不同缺陷和纹理的八个数据集评估了所提出的方法，该方法在较低的总体漏检率下获得了令人满意的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号