A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automated Inspection of Textile Fabrics Using Textural Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
High breakdown mixture discriminant analysis
Journal of Multivariate Analysis
Neural Computation
Adaptive mixtures of local experts
Neural Computation
Computer Vision and Image Understanding
Stitching defect detection and classification using wavelet transform and BP neural network
Expert Systems with Applications: An International Journal
Decision fusion for postal address recognition using belief functions
Expert Systems with Applications: An International Journal
Partially supervised learning by a credal EM approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Expert Systems with Applications: An International Journal
Wavelet-based defect detection in solar wafer images with inhomogeneous texture
Pattern Recognition
Hi-index | 12.05 |
A new approach for classification of circular knitted fabric defect is proposed which is based on accepting uncertainty in labels of the learning data. In the basic classification methodologies it is assumed that correct labels are assigned to samples and these approaches concentrate on the strength of categorization. However, there are some classification problems in which a considerable amount of uncertainty exists in the labels of samples. The core of innovation in this research has been usage of the uncertain information of labeling and their combination with the Dempster-Shafer theory of evidence. The experimental results show the robustness of the proposed method in comparison with usual classification techniques of supervised learning where the certain labels are assigned to training data.