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Browsing Paper Publication by Author "Asha, V"

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    Automatic Detection of Defects on Periodically Patterned Textures
    (2012-09-15T09:11:49Z) Asha, V; Bhajantri, N; Nagabhushan, P
    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 texture Defacts Using Texture Periodicity and Chi - Square Histogram Distance
    (2012-09-15T09:23:36Z) Asha, V; Bhajantri, N; Nagabhushan, P
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    Fabric Inspection using Gradient Space and its Energy
    (2015-11-27T06:59:40Z) Asha, V; Bhajantri, N U; Nagabhushan, P
    In this paper, we propose a machine vision algorithm for fabric inspection in patterned textures with the help of gradient space obtained using Newton’s forward difference scheme and its energy. Gradient space image is obtained from the input defective image and is split into several blocks of size same as that of the periodic unit of the input defective image. Energy of the gradient space image is used as input feature space for identifying defective and non defective periodic blocks using Ward’s hierarchical clustering. Experiments on real fabric images with defects show that the proposed method can be used for automatic detection of fabric defects in textile industries
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    Similarity Measures for Automatic Defect Detection on Patterned Textures
    (2012-12-26T06:26:22Z) Asha, V; Nagabhushan, P; Bhajantri, N. U.
    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.