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Fabric Defect Detection Using Steerable Pyramid


Affiliations
1 Department of Computer Science & Engineering, United Institute of Technology, Tamil Nadu, India
     

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In this paper, a novel idea is proposed for fabric defect detection. De-fects are detected in the fabric using steerable pyramid along with a defect detection algorithm. Various steerable pyramid of four size 256256, 128128, 6464, 3232 and with four orientation bands 00,450, 900, 1350 are used. Utilizing a Steerable pyramid proved ade-quate in the representation of fabric images in multi-scale and multi-orientations; thus allowing defect detection algorithms to run more effectively. Defect detection algorithm identifies and locates the im-perfection in the defective sample using the statistics mean and stan-dard deviation. This statistics represents the relative amount of inten-sity in the texture and is sufficient to measure defects in the current model .The obtained result are compared with the existing methods wavelet based system and with Gaussian and Laplacian pyramid.

Keywords

Fabric Automatic Visual Inspection, Steerable Pyramid, Feature Extractor, Defect Detector.
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  • Fabric Defect Detection Using Steerable Pyramid

Abstract Views: 181  |  PDF Views: 2

Authors

S. Mythili
Department of Computer Science & Engineering, United Institute of Technology, Tamil Nadu, India

Abstract


In this paper, a novel idea is proposed for fabric defect detection. De-fects are detected in the fabric using steerable pyramid along with a defect detection algorithm. Various steerable pyramid of four size 256256, 128128, 6464, 3232 and with four orientation bands 00,450, 900, 1350 are used. Utilizing a Steerable pyramid proved ade-quate in the representation of fabric images in multi-scale and multi-orientations; thus allowing defect detection algorithms to run more effectively. Defect detection algorithm identifies and locates the im-perfection in the defective sample using the statistics mean and stan-dard deviation. This statistics represents the relative amount of inten-sity in the texture and is sufficient to measure defects in the current model .The obtained result are compared with the existing methods wavelet based system and with Gaussian and Laplacian pyramid.

Keywords


Fabric Automatic Visual Inspection, Steerable Pyramid, Feature Extractor, Defect Detector.