Identification of Single and Double Jersey Fabrics Using Proximal Support Vector Machine

  • Abul Hasnat
  • Anindya Ghosh
  • Subhasis Das
  • Santanu Halder
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 335)


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.


Single jersey Double jersey Image processing Pattern classification Proximal support vector machine K-fold cross-validation 


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Copyright information

© Springer India 2015

Authors and Affiliations

  • Abul Hasnat
    • 1
  • Anindya Ghosh
    • 1
  • Subhasis Das
    • 1
  • Santanu Halder
    • 2
  1. 1.Government College of Engineering and Textile TechnologyBerhamporeIndia
  2. 2.Kalyani Government Engineering CollegeKolkataIndia

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