Fabric defect detection with LBP-GLMC

Kaynar, Oğuz; Işık, Yunus Emre; Görmez, Yasin; Demirkoparan, Ferhan

doi:10.1109/idap.2017.8090188

Cited by 20 publications

(12 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While some of these studies distinguish the images as defected/un-defected, some perform classification according to defect types. Gabor wavelets, [14][15][16] PCA, 14,15 independent component analysis (ICA), 17 the local binary pattern (LBP), 18,19 the gray level co-occurrence matrix (GLCM), 18,19 support vector machines (SVMs), [19][20][21][22][23] and k-nearest neighbors (KNN) 21,22 are used in the majority of fabric defect detection studies. The methods of the histogram, cooccurrence matrix, and shape descriptor are used for feature extraction, while the SVM is used for classification in the study of Murino et al 20 Images are divided into sub-windows in the study of Basibuyuk et al 24 Autoregressive coefficients are calculated using randomly selected un-defected images.…”

Section: Related Workmentioning

confidence: 99%

The effects of fusion-based feature extraction for fabric defect classification

Çıklaçandır

Utku

Özdemi̇r

2023

Textile Research Journal

View full text Add to dashboard Cite

Image processing has been employed in a variety of fields since the advent of image processing techniques. One of these fields is textiles. The existence of any defect in a fabric is one of the most important factors affecting the quality of the fabric. There are many types of fabric defects that can occur for various reasons. It is critical to figure out what caused the defect and fix it so that it does not occur again. Automation of fabric defect detection has recently attracted a great deal of interest in view of the development in artificial intelligence technology to be able to discover defects with a high degree of success and to limit the harm to the manufacturer. This study focuses on analyzing different feature extraction methods and different classifiers and discussing the advantages/disadvantages of the combinations and, unlike other studies, using feature fusion for feature extraction. Different cases have been created that handle fabric datasets from different angles and apply different methods of feature extraction (convolution neural network, minimum relevance and maximum redundancy) and classification (ensemble learning (EL), k-nearest neighbor, support vector machine (SVM)) for separating defected and un-defected patterned and un-patterned fabrics. ResNet18 is the convolution neural network-based model with the highest performance in feature extraction, while EL and the SVM allow us to achieve close and highly successful results in classification. When feature fusion is used, ResNet18 & GoogLeNet & SVM is the most successful combination compared to the others (94.66%).

show abstract

Section: Related Workmentioning

confidence: 99%

The effects of fusion-based feature extraction for fabric defect classification

Çıklaçandır

Utku

Özdemi̇r

2023

Textile Research Journal

View full text Add to dashboard Cite

show abstract

“…The LBP is another well-known feature extraction method. The number of features extracted in this algorithm is determined according to the size of the selected window [2]. The LBP is based on sequential binary comparisons between the pixel values of center and neighbors of it [20].…”

Section: Figure 2 the Flow Chart Of Pca Local Binary Pattern (Lbp)mentioning

confidence: 99%

“…There are some studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17] about the automatic systems for fabric defect classification. Some of the fabric factories that do automatic control carry out after production, while others do defect control during production.…”

Section: Table 5 Summary Table What Do We Already Know About This Topic?mentioning

confidence: 99%

See 1 more Smart Citation

A Suggestion System According to Fabric Control Time

YAŞAR

Utku

2022

Gazi University Journal of Science

View full text Add to dashboard Cite

show abstract

“…RBF ANNs, on the other hand, use radial-based activation functions and nonlinear cluster analysis in the transformation from the input layer to the hidden layer, unlike traditional ANN structures [13,14]. As with other ANN forms [15], the arrangement between the hidden and output layers continues to work.…”

Section: Radial-based Function Artificial Neural Networkmentioning

confidence: 99%

Computer-Aided Model for the Classification of Acute Inflammations via Radial-Based Function Artificial Neural Network

Kaya

2021

The Journal of Cognitive Systems

View full text Add to dashboard Cite

This study aimed to compare the classification performance of acute inflammation by applying the RBF ANN model on an open-access acute inflammation data set and determining the risk factors that may be associated with acute inflammation markers. Material and Methods:In the study, Nephritis of renal pelvis origin was classified using the open access "Acute Inflammation" data set RBF ANN model, and risk factors that could be associated were revealed. The success of RBF ANN is presented by different performance metrics. Results:The success of classifying Nephritis of renal pelvis origin with the RBF ANN model has been demonstrated to be excellent (AUC = 1, Accuracy = 100%). In addition, the RBF ANN model revealed that the most important variable among the risk factors that may be associated with Nephritis of renal pelvis origin is "temperature of patient". Conclusion:As a result, the obtained findings show that the RBF ANN model provides very successful predictions in the classification of Nephritis of renal pelvis origin. Also, it has been shown that the importance values of factors associated with Nephritis of renal pelvis origin are estimated with the RBF classification model and can be used safely in preventive medicine applications.

show abstract

Fabric defect detection with LBP-GLMC

Cited by 20 publications

References 8 publications

The effects of fusion-based feature extraction for fabric defect classification

The effects of fusion-based feature extraction for fabric defect classification

A Suggestion System According to Fabric Control Time

Computer-Aided Model for the Classification of Acute Inflammations via Radial-Based Function Artificial Neural Network

Contact Info

Product

Resources

About