Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Learning Texture Discrimination Masks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Increasing flexibility for automatic visual inspection: the general analysis graph
Machine Vision and Applications - special issue on high performance computing for industrial visual inspection
Tracking Facial Features using Gabor Wavelet Networks
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Gabor Wavelet Networks for Object Representation
Mustererkennung 2000, 22. DAGM-Symposium
On-line fabric-defects detection based on wavelet analysis
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Detection filters and algorithm fusion for ATR
IEEE Transactions on Image Processing
Unsupervised texture segmentation of images using tuned matched Gabor filters
IEEE Transactions on Image Processing
Fabric defect detection using morphological filters
Image and Vision Computing
Ellipsoidal decision regions for motif-based patterned fabric defect detection
Pattern Recognition
Fabric defect classification using radial basis function network
Pattern Recognition Letters
Robotics and Computer-Integrated Manufacturing
Review article: Automated fabric defect detection-A review
Image and Vision Computing
Fabric defect detection based on computer vision
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Automated defect detection in uniform and structured fabrics using Gabor filters and PCA
Journal of Visual Communication and Image Representation
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This paper studies the application of advanced computer image processing techniques for solving the problem of automated defect detection for textile fabrics. A new defect detection scheme is proposed, which consists of an odd symmetric real-valued Gabor filter, an even symmetric real-valued Gabor filter and one smoothing filter. In developing the scheme, the Gabor filters are designed on the basis of the texture features extracted optimally from a non-defective fabric image by using a Gabor wavelet network (GWN). The performance of the proposed defect detection scheme is evaluated off-line by using a set of fabric images taken from a database consisting of a wide variety of homogeneous fabric images. The results exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed scheme. To evaluate the performance of the proposed defect detection scheme further, real-time tests are conducted by using a prototyped automated defect detection system. The experimental results obtained further confirm the efficiency, effectiveness and robustness of the proposed detection scheme.