Defect Detection in Textiles with Co-occurrence Matrix as a Texture Model Description

2018 ◽  
pp. 216-226
Author(s):  
Karolina Nurzynska ◽  
Michał Czardybon
Keyword(s):  
Defect Detection ◽  
Model Description ◽  
Texture Model ◽  
Occurrence Matrix

A defect detection method for unpatterned fabric based on multidirectional binary patterns and the gray-level co-occurrence matrix

2019 ◽  
Vol 90 (7-8) ◽  
pp. 776-796 ◽  
Author(s):  
Feng Li ◽  
Lina Yuan ◽  
Kun Zhang ◽  
Wenqing Li
Keyword(s):  
Defect Detection ◽  
Detection Threshold ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Detection Scheme ◽  
Second Stage ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Different Characteristics

A new texture-feature description operator, called the multidirectional binary patterns (MDBP) operator, is proposed in this paper. The operator can extract the detailed distribution of textures in local regions by comparing the differences in the gray levels between neighboring pixels. Moreover, the texture expression ability is enhanced by focusing on the texture features in the linear neighborhood of the image in multiple directions. The MDBP operator was modified by introducing a “uniform” pattern to reduce the grayscale values in the image. Combining the “uniform” MDBP operator and the gray-level co-occurrence matrix, an unpatterned fabric-defect detection scheme is proposed, including texture-feature extraction and detection stages. In the first stage, the multidirectional texture-feature matrix of a nondefective fabric image is extracted, and then the detection threshold is determined based on the similarity between the feature matrices. In the second stage, the defect is detected with the detection threshold. The proposed method is adapted to various grayscale textile images with different characteristics and is robust to a wide variety of image-processing operations. In addition, it is invariant to grayscale changes, performs well when representing textures and detecting defects and has lower computational complexity than other methods.

Perceptron Neural Network Based Machine Learning Approaches for Leather Defect Detection and Classification

2020 ◽  
Vol 16 (6) ◽  
pp. 421-429
Author(s):  
Praveen Kumar Moganam ◽  
Denis Ashok Sathia Seelan
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Confusion Matrix ◽  
Texture Features ◽  
Learning Approaches ◽  
Data Set ◽  
Defect Identification ◽  
Inspection Process ◽  
Identification Approach ◽  
Occurrence Matrix

Detection of defects in a typical leather surface is a difficult task due to the complex, non-homogenous and random nature of texture pattern. This paper presents a texture analysis based leather defect identification approach using a neural network classification of defective and non-defective leather. In this work, Gray Level Co-occurrence Matrix (GLCM) is used for extracting different statistical texture features of defective and non-defective leather. Based on the labelled data set of texture features, perceptron neural network classifier is trained for defect identification. Five commonly occurring leather defects such as folding marks, grain off, growth marks, loose grain and pin holes were detected and the classification results of perceptron network are presented. Proposed method was tested for the image library of 1232 leather samples and the accuracy of classification for the defect detection using confusion matrix is found to be 94.2%. Proposed method can be implemented in the industrial environment for the automation of leather inspection process.

scholarly journals Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

2015 ◽  
Vol 15 (3) ◽  
pp. 226-232 ◽  
Author(s):  
Dandan Zhu ◽  
Ruru Pan ◽  
Weidong Gao ◽  
Jie Zhang
Keyword(s):  
Autocorrelation Function ◽  
Defect Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Template Image ◽  
Fabric Defect ◽  
Detection Window ◽  
Euclidean Distances ◽  
Dyed Fabric ◽  
Occurrence Matrix

Abstract In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

Crack-like defect detection and sizing from image segmentation through co-occurrence matrix analysis

Ultrasonics ◽  
1992 ◽  
Vol 30 (6) ◽  
pp. 359-363 ◽  
Author(s):  
J. Moysan ◽  
P. Benoist ◽  
G. Corneloup ◽  
I.E. Magnin
Keyword(s):  
Image Segmentation ◽  
Defect Detection ◽  
Matrix Analysis ◽  
Occurrence Matrix

An optimized skin texture model using gray-level co-occurrence matrix

2017 ◽  
Vol 31 (6) ◽  
pp. 1835-1853 ◽  
Author(s):  
Mahdi Maktabdar Oghaz ◽  
Mohd Aizaini Maarof ◽  
Mohd Foad Rohani ◽  
Anazida Zainal ◽  
Syed Zainudeen Mohd Shaid
Keyword(s):  
Gray Level ◽  
Skin Texture ◽  
Texture Model ◽  
Occurrence Matrix

scholarly journals A Hybrid Method of Textile Defect Detection using GLCM, LBP, SVD and Wavelet Transform

2020 ◽  
Vol 8 (6) ◽  
pp. 5356-5360
Keyword(s):  
Defect Detection ◽  
Local Binary Pattern ◽  
Defect Inspection ◽  
Image Processing Techniques ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Occurrence Matrix ◽  
Value Decomposition ◽  
Processing Techniques ◽  
Detection And Localization

A roll of fabric with defects can have a depreciation of 45 to 65% with respect to the original price. While some commercial solutions exist, automatic fabric defect detection remains an active field of development and research. The goal is to extract the characteristics of the texture of the fabric to detect defects contained using image processing techniques. To date, there is no standard method which ensures the detection of texture defects in fabrics with high precision. In the following work, the use of Singular Value Decomposition (SVD), Local Binary Pattern (LBP) and GrayLevel Co-Occurrence Matrix (GLCM) features of images for the identification of defects in textiles is presented, where the application of techniques for pre-processing is presented, and for the analysis of texture LBP and the GLCM in order to extract features and segmentation is done using SVD approach. This model makes it possible to obtain compact and precise detection of the faulty texture structures. Our method is capable of achieving very precise detection and localization of texture defects in the images of the Fabric-Defect-Inspection-GLSR database, while ensuring a reasonable processing time.

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