Similarity Measures for Automatic Defect Detection on Patterned Textures
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2012-12-26T06:26:22Z
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Abstract
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.,
Normalized 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
other all 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.
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International Journal of Image Processing and Vision Sciences, Similarity Measures for Automatic Defect Detection on Patterned Textures, V. Asha, P. Nagabhushan, N. U. Bhajantri