International Journal of Computer Vision
Modern Information Retrieval
Similarity between Euclidean and cosine angle distance for nearest neighbor queries
Proceedings of the 2004 ACM symposium on Applied computing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Refined Gaussian Weighted Histogram Intersection and Its Application in Number Plate Categorization
CGIV '06 Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Defect Detection in Patterned Fabrics Using Modified Local Binary Patterns
ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Motif-based defect detection for patterned fabric
Pattern Recognition
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Ellipsoidal decision regions for motif-based patterned fabric defect detection
Pattern Recognition
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Similarity measures are widely used in various applications such as information retrieval, image and object recognition, text retrieval, and web data search. In this paper, we propose similarity-based methods for defect detection on patterned textures using five different similarity measures, viz., normalised histogram intersection coefficient, Bhattacharyya coefficient, Pearson product-moment correlation coefficient, Jaccard coefficient and cosine-angle coefficient. Periodic blocks are extracted from each input defective image and similarity matrix is obtained based on the similarity coefficient of histogram of each periodic block with respect to itself and all other periodic blocks. Each similarity matrix is transformed into dissimilarity matrix containing true-distance metrics and Ward's hierarchical clustering is performed to discern between defective and defect-free blocks. Performance of the proposed method is evaluated for each similarity measure based on precision, recall and accuracy for various real fabric images with defects such as broken end, hole, thin bar, thick bar, netting multiple, knot, and missing pick.