Abstract
Single and double jersey knitted fabrics are different in many aspects, but it is difficult to identify them in open eye, and in textile industry, it is essential to identify them automatically. So far, no hands-on state-of-the-art technology has been adopted for identification of single and double jersey fabrics. This novel work endeavors to recognize these two kind of knitted fabrics by means of proximal support vector machine (PSVM) using the features extracted from gray level images of both fabrics. A k-fold cross-validation technique has been applied to assess the accuracy. The robustness, speed of execution, proven accuracy coupled with simplicity in algorithm holds the PSVM as a foremost classifier to recognize single and double jersey fabrics.
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Hasnat, A., Ghosh, A., Das, S., Halder, S. (2015). Identification of Single and Double Jersey Fabrics Using Proximal Support Vector Machine. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 335. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2217-0_33
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DOI: https://doi.org/10.1007/978-81-322-2217-0_33
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