Paper Info
Title
Fabric Defect Detection Using Salience Metric for Color Dissimilarity and Positional Aggregation.
Abstract
In this paper, inspired by an inherent characteristic of human visual system capable of recognizing salient regions from a complicated scene, we treat a defective region as a salient region in fabric images. A novel fabric defect detection method, which is based on saliency metric for color dissimilarity and positional aggregation, is proposed. In the method, the RGB color space of a given fabric image is first converted into the CIE L*a*b color space for feature representation. Then, the color dissimilarity and the positional distance between similar patches are jointly used to measure the defective values. To improve the contrast between the defective region and the non-defective region, a multi-scale analysis scheme performed on the pyramid images of the input fabric image is applied to calculate the defective values. Finally, the obtained defect map image is further enhanced by a certain threshold regarding to the obtained defective values. Thorough experimental results on several types of fabric images indicate the effectiveness of the newly proposed method.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2868059
IEEE ACCESS
Keywords
Field
DocType
Color dissimilarity,defect map,fabric defect detection (FDD),K-nearest neighbor (KNN),multi-scale
Color space,Pattern recognition,Human visual system model,Salience (neuroscience),Visualization,Computer science,RGB color space,Feature extraction,Pyramid,Artificial intelligence,Salient,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
1
PageRank 
References 
Authors
0.35
0
6
Name
Order
Citations
PageRank
Kaibing Zhang156823.60
Yadi Yan210.35
Pengfei Li341.16
Junfeng Jing481.55
Xiuping Liu515618.74
Zhen Wang67628.98