Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
A fractal image analysis systems for fabric inspection based on a box-counting method
Computer Networks and ISDN Systems - Special issue on graphics research and education on the World Wide Web
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Support Vector Data Description
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Fractal-Based Description of Natural Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Two-class support vector data description
Pattern Recognition
Subjective measurement of cosmetic defects using a Computational Intelligence approach
Engineering Applications of Artificial Intelligence
Inspection of surface defects in copper strip based on machine vision
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
Review article: Automated fabric defect detection-A review
Image and Vision Computing
Expert Systems with Applications: An International Journal
Inspection of surface defects in copper strip using multivariate statistical approach and SVM
International Journal of Computer Applications in Technology
Data driven process modeling and simulation: an applied case study
Proceedings of the 2013 Summer Computer Simulation Conference
| Hi-index | 0.00 |
Computer-vision-based automatic detection of fabric defects is one of the difficult one-class classification tasks in the real world. To overcome the incapacity of a single fractal feature in dealing with this task, multiple fractal features have been extracted in the light of the theory of and problems present in the box-counting method as well as the inherent characteristics of woven fabrics. Based on statistical learning theory, the up-to-date support vector data description (SVDD) is an excellent approach to the problem of one-class classification. A robust new scheme is presented in this paper for optimally selecting values of the parameters especially that of the scale parameter of the Gaussian kernel function involved in the training of the SVDD model. Satisfactory experimental results are finally achieved by jointly applying the extracted multiple fractal features and SVDD to the detection of defects from several datasets of fabric samples with different texture backgrounds.