基于局部最优分析的纺织品瑕疵检测方法

doi:10.16451/j.cnki.issn1003-6059.201802010

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Abstract

Aiming at the detection of textile defects with complex periodic patterns, an unsupervised fabric defect detection method based on modified Markov random field model is proposed. The defects of periodic textile images are detected and the areas of defect are judged via the Markov neighborhood feature. The minimum image block computing unit of Markov random field is determined by combining the segmentation of periodic image, and the computational complexity of the algorithm is reduced. In the definition of the random field potential function, the difference of adjacent image blocks is comprehensively taken into account. The location of defect area is judged by the global characteristics of the Markov random field. The concept of fuzzy similarity relation matrix is introduced to solve the parameters of the improved Markov random field model, and the local energy of all image blocks is optimized.Experiments show that the proposed defect detection method gains high recall.

Key words： Markov Random Fields Local Optimum Similarity Relation Unsupervised Defect
Detection

Received: 02 October 2017

ZTFLH:

TP 391

Fund:

Supported by National Natural Science Foundation of China(No.61170121), Changzhou Key Laboratory of High Technology Research(No.CM20153001)

About author:: LIU Wei, master student. His research interests include computer vision.CHANG Xingzhi, Ph.D., associate senior engineer. His research interests include computer vision.LIANG Jiuzhen(Corresponding author), Ph.D., professor. His research interests include computer vision.JIA Liang, master, assistant professor. His research interests include computer vision.GU Chengxi, master student. His research interests include computer vision.

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	LIU Wei
	CHANG Xingzhi
	LIANG Jiuzhen
	JIA Liang
	GU Chengxi

Cite this article:

LIU Wei,CHANG Xingzhi,LIANG Jiuzhen等. Fabric Defect Detection Based on Local Optimum Analysis[J]. , 2018, 31(2): 182-189.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201802010 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I2/182

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