Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
An automated inspection system for textile fabrics based on Gabor filters
Robotics and Computer-Integrated Manufacturing
Defect detection in textured materials using optimized filters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
Review article: Automated fabric defect detection-A review
Image and Vision Computing
Expert Systems with Applications: An International Journal
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In this paper, a novel defect detection scheme based on morphological filters is proposed to tackle the problem of automated defect detection for woven fabrics. In the proposed scheme, important texture features of the textile fabric are extracted using a pre-trained Gabor wavelet network. These texture features are then used to facilitate the construction of structuring elements in subsequent morphological processing to remove the fabric background and isolate the defects. Since the proposed defect detection scheme requires a few morphological filters only, the amount of computational load involved is not significant. The performance of the proposed scheme is evaluated by using a wide variety of homogeneous textile images with different types of common fabric defects. The test results obtained exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed detection scheme. In addition, the proposed detection scheme is further evaluated in real time by using a prototyped automated inspection system.