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Proceedings Paper

Automated pilling detection and fuzzy classification of textile fabrics
Author(s): Iqbal M. Dar; Waqar Mahmood; George Vachtsevanos
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Paper Abstract

In the textile industry, the degree of fabric pilling is subjectively determined by human inspectors resulting in inconsistent quality control. The observed resistance to pilling is reported on an arbitrary scale ranging from No. 5 (no pillings) to No. 1 (very severe pilling). This paper presents a system and a methodology that counts the number of pillings on textile fabric samples automatically and classifies them into one of the pre-defined classes with repeatable accuracy while accounting for the human judgment by allowing the determination of the degree of confidence assigned to the sample's membership in each class. The system consists of an apparatus; an imaging and data processing software procedure for counting the number of pillings; and a methodology for classifying the fabric samples into one of the pre-defined classes with repeatable accuracy while accounting for human judgment. A CCD camera is used to capture successive gray scale images of the fabric sample. A series of segmentation, Radon transform, morphological filtering, and detrending operations are applied to the fabric images to determine the true pilling count. The structuring element for the morphological operations is designed such that fuzz balls (which are not pillings) are filtered. Using fuzzy membership functions, the fabric pilling count is mapped to fabric pilling resistance rating. The system has been successfully tested on a large number of fabric samples with different shades and textures provided by the textile industry.

Paper Details

Date Published: 15 April 1997
PDF: 11 pages
Proc. SPIE 3029, Machine Vision Applications in Industrial Inspection V, (15 April 1997); doi: 10.1117/12.271245
Show Author Affiliations
Iqbal M. Dar, Ciena Corp. (United States)
Waqar Mahmood, Georgia Institute of Technology (United States)
George Vachtsevanos, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3029:
Machine Vision Applications in Industrial Inspection V
A. Ravishankar Rao; Ning S. Chang, Editor(s)

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