A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multichannel Texture Analysis Using Localized Spatial Filters
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
Texture segmentation using wavelet transform
Pattern Recognition Letters
Wavelet based methods on patterned fabric defect detection
Pattern Recognition
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
Fabric defect classification using radial basis function network
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
A hybrid particle swarm optimization and its application in neural networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Feedback controlled particle swarm optimization and its application in time-series prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In the textile and clothing industry, much research has been conducted on fabric defect automatic detection and classification. However, little research has been done to evaluate specifically the stitching defects of a garment. In this study, a stitching detection and classification technique is presented, which combines the improved thresholding method based on the wavelet transform with the back propagation (BP) neural network. The smooth subimage at a certain resolution level using the pyramid wavelet transform was obtained. The study uses the direct thresholding method, which is based on wavelet transform smooth subimages from the use of a quadrant mean filtering method, to attenuate the texture background and preserve the anomalies. The images are then segmented by thresholding processing and noise filtering. Nine characteristic variables based on the spectral measure of the binary images were collected and input into a BP neural network to classify the sample images. The classification results demonstrate that the proposed method can identify five classes of stitching defects effectively. Comparisons of the proposed new direct thresholding method with the direct thresholding method based on the wavelet transform detailed subimages and the automatic band selection for wavelet reconstruction method were made and the experimental results show that the proposed method outperforms the other two approaches.