scholarly journals Generic Wrapper Based Model using Haralick Features for Silk Fabric Defect Classification

2021 ◽  
Vol 23 (11) ◽  
pp. 867-878
Author(s):  
Ms. Shweta Loonkar ◽  
◽  
Dhirendra S. Mishra ◽  
Surya S. Durbha ◽  
◽  
...  
Keyword(s):  
Defect Detection ◽  
Control Unit ◽  
Gabor Transform ◽  
Defect Classification ◽  
Silk Fabric ◽  
Fabric Defect ◽  
Segmented Images ◽  
Wrapper Method ◽  
Haralick Features

Quality control unit of fabric industry looks for the effective defect detection methodology. The research is required to be done in this area to develop such solution. Various models based on combination of suitable feature extraction, selection and classification approaches need to be experimented out for the same. This paper attempts to experiment and provide such models mainly based on generic wrapper based selection approaches. Widely used broader range of Haralick features are prominently used for detection and classification of defects in this research. It also attempts to identify the suitability of these features based on segmented images provided as an input. The research has been carried on TILDA Dataset consisting of 800 Silk Fabric Images with eight different defects present on it and each carrying 100 images per defect. Models generated using generic wrapper based approach has also been compared with the Gabor Transforms. Then identification of suitable Haralick Features for particular type of defects has been carried out. In this 68% classification accuracy has been achieved using generic wrapper method and 40 % accuracy has been achieved using Gabor Transform with respect to fourteen Haralick Features and seven types of defects.

Computer vision for automatic detection and classification of fabric defect employing deep learning algorithm

2019 ◽  
Vol 31 (4) ◽  
pp. 510-521 ◽  
Author(s):  
Pandia Rajan Jeyaraj ◽  
Edward Rajan Samuel Nadar
Keyword(s):  
Defect Detection ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Data Sets ◽  
Defect Classification ◽  
Content Type ◽  
Deep Learning Algorithm ◽  
Average Accuracy ◽  
Fabric Defect

Purpose The purpose of this paper is to focus on the design and development of computer-aided fabric defect detection and classification employing advanced learning algorithm. Design/methodology/approach To make a fast and effective classification of fabric defect, the authors have considered a characteristic of texture, namely its colour. A deep convolutional neural network is formed to learn from the training phase of various defect data sets. In the testing phase, the authors have utilised a learning feature for defect classification. Findings The improvement in the defect classification accuracy has been achieved by employing deep learning algorithm. The authors have tested the defect classification accuracy on six different fabric materials and have obtained an average accuracy of 96.55 per cent with 96.4 per cent sensitivity and 0.94 success rate. Practical implications The authors had evaluated the method by using 20 different data sets collected from different raw fabrics. Also, the authors have tested the algorithm in standard data set provided by Ministry of Textile. In the testing task, the authors have obtained an average accuracy of 94.85 per cent, with six defects being successfully recognised by the proposed algorithm. Originality/value The quantitative value of performance index shows the effectiveness of developed classification algorithm. Moreover, the computational time for different fabric processing was presented to verify the computational range of proposed algorithm with the conventional fabric processing techniques. Hence, this proposed computer vision-based fabric defects detection system is used for an accurate defect detection and computer-aided analysis system.

Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method

2013 ◽  
Vol 104 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Junfeng Jing ◽  
Huanhuan Zhang ◽  
Jing Wang ◽  
Pengfei Li ◽  
Jianyuan Jia
Keyword(s):  
Defect Detection ◽  
Gabor Filters ◽  
Defect Classification ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

Fabric Defect Classification Based on Local Region Features and SVM

2012 ◽  
Vol 182-183 ◽  
pp. 634-638
Author(s):  
Yi Hong Li ◽  
Zhao Yang Lu ◽  
Jing Li ◽  
Ling Ling Cui
Keyword(s):  
Support Vector Machines ◽  
Recognition Rate ◽  
Local Region ◽  
Small Samples ◽  
Support Vector ◽  
Defect Classification ◽  
Vector Machines ◽  
Fabric Defect ◽  
Learning Machine

The big differences of the texture and shapes in the same type and certain similarities among heterogeneous types result in the difficult classification of fabric defects. Compared with traditional global statistical method, we put up a new solution, which makes use of the fabric defect local region features to keep the defect property and defect classification by Support Vector Machines (SVM). Based on small-samples learning machine of SVM, we obtain a good performance of less computational load and high recognition rate.

An effective technique for diabetic retinopathy using hybrid machine learning technique

2021 ◽  
pp. 096228022098354
Author(s):  
N Satyanarayana Murthy ◽  
B Arunadevi
Keyword(s):  
Diabetic Retinopathy ◽  
Short Term Memory ◽  
Early Recognition ◽  
Hybrid Techniques ◽  
Specificity And Sensitivity ◽  
Retinal Blood Vessels ◽  
Segmented Images ◽  
Fuzzy C Means Clustering ◽  
Working Region

Diabetic retinopathy (DR) stays as an eye issue that has continuously developed in individuals who experienced diabetes. The complexities in diabetes cause harm to the vein at the back of the retina. In outrageous cases, DR could swift apparition disaster or visual impairment. This genuine impact had the option to charge through convenient treatment and early recognition. As of late, this issue has been spreading quickly, particularly in the working region, which in the end constrained the interest of an analysis of this disease from the most prompt stage. Therefore, that are castoff to protect the progressions of this disorder, revealing of the retinal blood vessels (RBVs) play a foremost role. The growth of an abnormal vessel leads to the development steps of DR, where it can be well known by extracting the RBV. The recognition of the BV for DR by developing an automatic approach is a major aim of our research study. In the proposed method, there are two major steps: one is segmentation and the second one is classification of affected retinal BV. The proposed method uses the Kinetic Gas Molecule Optimization based on centroid initialization used for the Fuzzy C-means Clustering. In the classification step, those segmented images are given as input to hybrid techniques such as a convolution neural network with bidirectional-long short-term memory (CNN with Bi-LSTM). The learning degree of Bi-LSTM is revised by using the self-attention mechanism for refining the classification accuracy. The trial consequences disclosed that the mixture algorithm achieved higher accuracy, specificity, and sensitivity than existing techniques.

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