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Comparative Analysis of Fabric Fault Detection Using Hybrid Approach

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ICCCE 2019

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 570))

Abstract

This paper focuses on the fabric fault detection for variable sized textile images collected from textile industry. This paper presents the comparative analysis of Fabric Detection using hybrid approach where GLCM, Gabor Wavelet technique is used for image extraction and Random Forest Decision technique is used for image classification. The texture is observed as one of the utmost significant feature in the process of analysis of image and recognition of patterns. The incorporation of GLCM and Gabor Wavelet is being applied in order to obtain the best feature images of fabrics. The co-occurrence matrix has better processing effect for global region of images. Similarly, in attaining several level scales. Several level directional and native information in frequency domain Gabor Wavelet results are found excellent in performing the work. To categorize the defective and non-defective images into defective or non-defectiveness of the intended fabric image and in detecting the same the classification phase involves the Random forest classifier involved.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering TSEC, University of Mumbai, Mumbai, India

    Nilesh T. Deotale & Tanuja Sarode

Authors
  1. Nilesh T. Deotale
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  2. Tanuja Sarode
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Corresponding author

Correspondence to Nilesh T. Deotale .

Editor information

Editors and Affiliations

  1. BioAxis DNA Research Centre (P) Ltd, Hyderabad, India

    Amit Kumar

  2. Dynexsys, Sydney, NSW, Australia

    Stefan Mozar

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Cite this paper

Deotale, N.T., Sarode, T. (2020). Comparative Analysis of Fabric Fault Detection Using Hybrid Approach. In: Kumar, A., Mozar, S. (eds) ICCCE 2019. Lecture Notes in Electrical Engineering, vol 570. Springer, Singapore. https://doi.org/10.1007/978-981-13-8715-9_44

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  • DOI: https://doi.org/10.1007/978-981-13-8715-9_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8714-2

  • Online ISBN: 978-981-13-8715-9

  • eBook Packages: EngineeringEngineering (R0)

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