Motif-based defect detection for patterned fabric

Ngan, Henry Y. T.; Pang, G.K.H.; Yung, N.H.C.

doi:10.1016/j.patcog.2007.11.014

Cited by 54 publications

(51 citation statements)

References 42 publications

(98 reference statements)

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“…Methods that are found in literature for the inspection of patterned texture images include the traditional image subtraction methods [10][11][12], the method of golden image subtraction (GIS) [5], the method of wavelet-preprocessed golden image subtraction (WGIS) [5], the method of Direct-Thresholding (DT) based on wavelet transform [5], the Bollinger Bands method [6], the Regular Bands method [3], the Local Binary Pattern (LBP) method [7], and the motif-based methods [4,8,9].…”

Section: Previous Methodsmentioning

confidence: 99%

“…Ngan et al [4,8,9] developed motif-based methods for detecting defective lattices from 16 out of 17 wallpaper groups based on energy and the variance of the hand-located lattices. Minimum-maximum decision boundaries (rectangular boundaries) are obtained in an energyvariance space from several defect-free test images using hand-located defect-free and defective lattices that are said to be composed of motifs [4,8].…”

Section: Previous Methodsmentioning

confidence: 99%

“…Minimum-maximum decision boundaries (rectangular boundaries) are obtained in an energyvariance space from several defect-free test images using hand-located defect-free and defective lattices that are said to be composed of motifs [4,8]. The energy of the moving subtraction be-tween a motif and its circular shift matrices is derived using a norm-metric measurement and the variance of the energies for all motifs is obtained.…”

Section: Previous Methodsmentioning

confidence: 99%

“…The Jensen-Shannon Divergence metrics are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained using (8). The dissimilarity matrices thus obtained are shown in Fig.…”

Section: Illustration Of the Algorithmmentioning

confidence: 99%

“…As a result, most of the methods in literature depend on the training stage by using numerous defect-free samples for obtaining decision-boundaries or thresholds prior to the detection of defects (i.e., [3][4][5][6][7][8][9]). Moreover, methods involving pattern matching and the image subtraction approach perform a pixel-by-pixel comparison, which is inherently sensitive to noises, misalignments, and distortions [8]. Noises, distortions, and misalignments are very common in real fabric images.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automatic Detection of Texture-defects using Texture-periodicity and Jensen-Shannon Divergence

Asha¹,

Bhajantri²,

Nagabhushan³

2012

Journal of Information Processing Systems

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Abstract-In this paper, we propose a new machine vision algorithm for automatic defect detection on patterned textures with the help of texture-periodicity and the JensenShannon Divergence, which is a symmetrized and smoothed version of the KullbackLeibler Divergence. Input defective images are split into several blocks of the same size as the size of the periodic unit of the image. Based on histograms of the periodic blocks, Jensen-Shannon Divergence measures are calculated for each periodic block with respect to itself and all other periodic blocks and a dissimilarity matrix is obtained. This dissimilarity matrix is utilized to get a matrix of true-metrics, which is later subjected to Ward's hierarchical clustering to automatically identify defective and defect-free blocks.Results from experiments on real fabric images belonging to 3 major wallpaper groups, namely, pmm, p2, and p4m with defects, show that the proposed method is robust in finding fabric defects with a very high success rates without any human intervention

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