Texture segmentation using Gabor modulation/demodulation
Pattern Recognition Letters
Moment-based texture segmentation
Pattern Recognition Letters
A Fast Image Processing Algorithm for Quality Control of Woven Textiles
Mustererkennung 1998, 20. DAGM-Symposium
Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Empirical Evaluation of Generalized Cooccurrence Matrices
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motif-based defect detection for patterned fabric
Pattern Recognition
International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
Recycled paper visual indexing for quality control
Expert Systems with Applications: An International Journal
A new approach for image processing in foreign fiber detection
Computers and Electronics in Agriculture
Associating visual textures with human perceptions using genetic algorithms
Information Sciences: an International Journal
New approach for segmentation and pattern recognition of jacquard images
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Small object detection in cluttered image using a correlation based active contour model
Pattern Recognition Letters
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Quality control is one of the basic issues in textile industry. Analysis of texture content in digital images plays an important role in the automated visual inspection of textile images to detect their defects. In this paper, a system for automated visual inspection of textiles is discussed. A detailed system configuration is presented and a fault detection algorithm is proposed. Industrial vision systems must operate in real-time, produce a low false alarm rate and be flexible to accommodate variations in inspection sites. This was the rationale behind developing a detection algorithm which employs simple statistical features (mean, variance, median). The intent was to utilize such features to make the calculations simple and fast for the system to be suitable for real-time applications. The performance of the system was evaluated on plain fabrics with different types of textile flaws. The results indicate that the system can detect flaws which vary drastically in physical dimension and nature with a very low false detection rate.