NDLI: Modelling of Plain Weave Fabric Structure and Its Use in Fabric Defect Identification

Content Provider	IEEE Xplore Digital Library
Author	Vaddin, J. Subbaraman, S.
Copyright Year	2014
Description	Author affiliation: Electron. Dept., Walchand Coll. of Eng., Sangli, India (Subbaraman, S.) \|\| Electron. Dept., Textile & Eng. Inst., Ichalkaranji, India (Vaddin, J.)
Abstract	Texture is an inherent property of a fabric whose periodicity can be extracted from DC Suppressed Fourier Power Spectrum Sum (DCSFPSS). Periodicity can be used as one of the fabric quality parameters to detect the woven fabric defects. This paper focuses on modeling periodicity of a plain weave fabric based on DCSFPSS and using this model to detect the fabric defects. The nonparametric and parametric modeling were experimented on 1-D DCSFSS data as a signal where the effectiveness of parametric method in modeling normal fabric was evident. Parametric methods viz., Autoregressive (AR), and Autoregressive Moving Average (ARMA) models were tested on DCSFPSS of a normal fabric image. Performance parameter viz., %fit function for u direction of DCSFPSS was found to be 97.1%/93.8/95.4 for ARMA (64,64) / ARMA (32,32) /AR (32) indicating superiority of ARMA (64,64) over others but found to be computationally complex. For fabric defect detection, a comparatively simple AR (32) model showed for u/v direction of DCSFPSS, a fit function spread of 5%/5% for normal sample against that of ~43%/62% for loose weft defect sample. These facts justify that, a simple AR (32) models well the periodicity of the fabric for u/v direction of DCSFPSS and conclusively differentiates defective fabric from normal plain fabric samples.
Starting Page	132
Ending Page	137
File Size	372887
Page Count	6
File Format	PDF
e-ISBN	9781479974122
DOI	10.1109/EMS.2014.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-21
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	autoegresssive moving average Computational modeling Buildings Predictive models DCSFPSS loss function Fabrics Weaving Data models AIC fit function autoregresssive Autoregressive processes
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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