Automatic Detection of Defects on Periodically Patterned Textures
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Date
2012-09-15T09:11:49Z
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Abstract
Defect detection is a major concern in quality control of various products in
industries. This paper presents two different machine-vision based methods for detecting
defects on periodically patterned textures. In the first method, input defective image is split
into several blocks of size same as the size of the periodic unit of the image and chi-square
histogram distances of each periodic block with respect to itself and all other periodic
blocks are calculated to get a dissimilarity matrix. This dissimilarity matrix is subjected to
Ward’s hierarchical clustering to automatically identify defective and defect-free blocks.
The second method of defect detection is based on Universal Quality Index which is a
measure of loss of correlation, luminance distortion and contrast distortion between any
two signals. Quality indices of a periodic block with respect to itself and all other periodic
blocks are calculated to get a similarity matrix containing quality indices. Specific
variances of the periodic blocks are derived from the quality index matrix through orthogonal
factor model based on eigen decomposition. These variances are subjected to Ward’s
hierarchical clustering to automatically identify defective and defect-free blocks. Results
of experiments on real fabric images with defects show that the defect detection methods
based on chi-square histogram distance and universal quality index yield a success rate of
98.6% and 97.8% respectively.
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Automatic Detection of Defects on Periodically Patterned Textures, Asha, V, Nagabhushan, P, Bhajantri, N., Journal of Information Processing System