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Detection of fabric defects based on frequency-tuned salient algorithm

Detection of fabric defects based on frequency-tuned salient algorithm
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摘要 The correct rate of detection for fabric defect is affected by low contrast of images. Aiming at the problem,frequencytuned salient map is used to detect the fabric defect. Firstly,the images of fabric defect are divided into blocks. Then,the blocks are highlighted by frequency-tuned salient algorithm. Simultaneously,gray-level co-occurrence matrix is used to extract the characteristic value of each rectangular patch. Finally,PNN is used to detect the defect on the fabric image. The performance of proposed algorithm is estimated off-line by two sets of fabric defect images. The theoretical argument is supported by experimental results. The correct rate of detection for fabric defect is affected by low contrast of images. Aiming at the problem,frequencytuned salient map is used to detect the fabric defect. Firstly,the images of fabric defect are divided into blocks. Then,the blocks are highlighted by frequency-tuned salient algorithm. Simultaneously,gray-level co-occurrence matrix is used to extract the characteristic value of each rectangular patch. Finally,PNN is used to detect the defect on the fabric image. The performance of proposed algorithm is estimated off-line by two sets of fabric defect images. The theoretical argument is supported by experimental results.
作者 王传桐 Hu Feng Xu Qiyong
机构地区 武汉纺织大学机械学院 School of Mechanical Engineering and Automation
出处 《石化技术》 CAS 2017年第4期103-103,共1页 Petrochemical Industry Technology
关键词 FABRIC defect frequency-tuned salient ALGORITHM gray-level CO-OCCURRENCE matrix PNN fabric defect frequency-tuned salient algorithm gray-level co-occurrence matrix PNN
分类号 TS101.97 [轻工技术与工程—纺织工程]
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石化技术
2017年 第4期

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