Please use this identifier to cite or link to this item:
Title: Fabric defect detection using linear filtering and morphological operations
Authors: Çelik, H İbrahim
Dülger, L Canan
Topalbekiroğlu, Mehmet
Keywords: Denim fabric;Fabric defect detection;Image processing;Linear filtering;Morphological operation;Neural network
Issue Date: Sep-2014
Publisher: NISCAIR-CSIR, India
Abstract: An algorithm with linear filters and morphological operations has been proposed for automatic fabric defect detection. The algorithm is applied off-line and real-time to denim fabric samples for five types of defects. All defect types have been detected successfully and the defective regions are labeled. The defective fabric samples are then classified by using feed forward neural network method. Both defect detection and classification application performances are evaluated statistically. Defect detection performance of real time and off-line applications are obtained as 88% and 83% respectively. The defective images are classified with an average accuracy rate of 96.3%.
Page(s): 254-259
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.39(3) [September 2014]

Files in This Item:
File Description SizeFormat 
IJFTR 39(3) 254-259.pdf186.33 kBAdobe PDFView/Open

Items in NOPR are protected by copyright, with all rights reserved, unless otherwise indicated.