NDLI: Fabric defects detecting and rank scoring based on Fisher criterion discrimination

Content Provider	IEEE Xplore Digital Library
Author	Sheng-Wang Li Li-Wei Guo Chun-Hua Li
Copyright Year	2009
Description	Author affiliation: College of Information Science and Technology, Hebei University of Science and Technology, Shijiazhuang 050018, China (Sheng-Wang Li; Li-Wei Guo; Chun-Hua Li)
Abstract	Automatic texture defect detection is highly important for many fields of visual inspection. This paper studies the application of advanced computer image processing techniques for solving the problem of automated defect detection for textile fabrics. The approach is used for the quality inspection of local defects embedded in homogeneous textured surfaces. Above all, the size of the basic texture units of the fabric image is acquired by calculating auto correlation function in weft direction and in wrap direction. Then the sizes of the basic texture units are taken as criterion to segment the fabric image. During scanning the fabric texture image, the basic units are segmented. And the Fisher criterion discriminator is used to assign each unit to a class at the same time. Afterwards, the fabric detects are measured according to the relationship of the suffix of the image pixel and the scale of the image and ranked scale by comparing with America Four Points System. Experiments with real fabric image data show that it is effective.
Starting Page	2560
Ending Page	2563
File Size	208874
Page Count	4
File Format	PDF
ISBN	9781424437023
DOI	10.1109/ICMLC.2009.5212106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fabrics Inspection Image segmentation Application software Computer applications Image processing Textiles Surface texture Autocorrelation Pixel Fabric defect rating Pattern recognition Auto correlation function Fisher criterion discrimination Fabric defect detection
Content Type	Text
Resource Type	Article

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