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254-259An 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%
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