Human visual system can identify larger defects taking place on the woven fabric. But it is very difficult to classify and identify the small fabric defects by a human inspector. In the textile industries the defect detection by a human inspector affects the production tremendously. Thus this paper gives a solution of this problem by developing an automatic fabric defect detection system, based on the computer vision. The sub image based PCA method is applied for the extraction of the feature from the training and test fabric images and the multi-class SVM classifier is used for carrying out the classification task. The method is tested on the standard TILDA database of fabric defect and a success rate of 96.36% is achieved. © 2012 IEEE.

A BASU

J K CHANDRA

P K BANERJEE

S BHATTACHARYYA

A K DATTA

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Sub image based eigen fabrics method using multi-class SVM classifier for the detection and classification of defects in woven fabric

2012 3rd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012

In this paper a new approach for the detection of defects in woven fabric is presented where the singular value decomposition (SVD) method is used. SVD basically removes the interlaced grating structure of the waft and warp of the fabric leaving aside the defective part of the fabric. An intensity threshold value along with the module of definite size is considered for the binarization of the background free fabric image. Finally, for the removal of the noise from the binary fabric image the morphological opening operation with the suitable structuring element is performed. The technique is tested on 287 fabric samples consisting of five different types of defects in three types of woven fabrics from TILDA database. 94.08% success rate of detection of defects is achieved. © 2011 IEEE.

2011 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2011

Singular value decomposition method for the detection of defects in woven fabric refined by morphological operation

Introduction to Texture Analysis

Application of Diffraction to Texture Analysis

IEEE Transactions on Industrial Electronics

Computer-Vision-Based Fabric Defect Detection: A Survey

The Nature of Statistical Learning Theory

Proceedings of the fifth annual workshop on Computational learning theory  - COLT '92

A training algorithm for optimal margin classifiers

Journal	2012 3rd International Conference on Computing, Communication and Networking Technologies, ICCCNT 2012
Publisher	-