Feature extraction and classification of woven fabric using optimized Haralick parameters: A rough set based approach

Jayanta K. Chandra, Madhumanti Majumdar, Sourish Sarkar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Classification of fabric samples into classes is highly required for automatic fabric inspection systems, as many of the fabric defects are defined relative to the fabric classes. The texture of the fabric surface is the best way to represent a fabric class, corresponding to which the statistical measures are the Haralick parameters. As all of the Haralick parameters are not responsible for fabric classification and there are no universal Haralick parameters for classifying all types of fabric samples, so it is necessary to determine a subset of Haralick parameters that gives best classification result for the fabric classes under consideration. This subset of Haralick parameters is termed as optimized Haralick parameters of the fabric classes under consideration, which has been determined by using the rough set theory. The developed system has been tested on TILDA database and its superiority with respect to the non-optimized Haralick parameters is established in terms of classification result and separability index.

Original languageEnglish (US)
Title of host publication2016 2nd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages541-545
Number of pages5
ISBN (Electronic)9781509000357
DOIs
StatePublished - Jul 14 2016
Event2nd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2016 - Kolkata, India
Duration: Jan 28 2016Jan 30 2016

Publication series

Name2016 2nd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2016

Other

Other2nd International Conference on Control, Instrumentation, Energy and Communication, CIEC 2016
CountryIndia
CityKolkata
Period1/28/161/30/16

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Biomedical Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Control and Optimization
  • Instrumentation

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