Automated Inspection of Textile Fabrics Using Textural Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for Texture Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-class pattern classification using neural networks
Pattern Recognition
An automated inspection system for textile fabrics based on Gabor filters
Robotics and Computer-Integrated Manufacturing
Stitching defect detection and classification using wavelet transform and BP neural network
Expert Systems with Applications: An International Journal
Intelligent visual recognition and classification of cork tiles with neural networks
IEEE Transactions on Neural Networks
Wavelet based methods on patterned fabric defect detection
Pattern Recognition
Identifying and Locating Surface Defects in Wood: Part of an Automated Lumber Processing System
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Pyramidal Neural Network For Visual Pattern Recognition
IEEE Transactions on Neural Networks
Review article: Automated fabric defect detection-A review
Image and Vision Computing
InstanceRank based on borders for instance selection
Pattern Recognition
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In this paper, a new approach for fabric defect classification using radial basis function (RBF) network improved by Gaussian mixture model (GMM) is investigated. First, the gray level arrangement in the neighborhood of each pixel is extracted as the feature. This raw feature is subject to principal component analysis (PCA) which adopts the between class scatter matrix as the generation matrix to eliminate the variance within the same class. Second, the RBF network with Gaussian kernel is used as the classifier because of the nonlinear discrimination ability and support for multi-output. To train the classifier, GMM is introduced to cluster the feature set and precisely estimate the parameter in Gaussian RBF, in which each cluster strictly conforms to a multi-variance Gaussian distribution. Thus the parameter of each kernel function in RBF network can be acquired from a corresponding cluster. The proposed algorithm is experimented on fabric defect images with nine classes and achieves superior performance, which proves its utility in practice.