Fabric Surface Defect Detection Based on GMRF Model

被引：7
|
作者：
Xu, Yichen [1 ]
Meng, Fanwu [1 ]
Wang, Lizhong [1 ]
Zhang, Mingyi [1 ]
Wu, Changshuo [1 ]
机构：
[1] Beijing Inst Technol, Sch Mech Engn, Beijing, Peoples R China
来源：
PROCEEDINGS OF 2021 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INFORMATION SYSTEMS (ICAIIS '21) | 2021年
关键词：
Fabric surface defect detection; GMRF; machine vision; CLASSIFICATION;
D O I：
10.1145/3469213.3471336
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Fabric defect detection is a challenging task due to unpredictable forms of the fabric defect. This paper presents a method based on Gaussian Markov Random Field (GMRF) model which automatically inspects fabric surface defects. This paper presents a method for detecting unpredictable fabric defects based on machine vision through two steps. First, the parameter distribution of GMRF model is achieved from defect-free fabric images. Second, the image of the fabric patch to be inspected is divided into fixed-size non-overlapping windows, and each window is classified as defective or non-defective according to the GMRF model parameters of each window.
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收藏
页数：4
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