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Proceedings Paper

Statistical modeling, detection, and segmentation of stains in digitized fabric images
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Paper Abstract

This paper will describe a novel and automated system based on a computer vision approach, for objective evaluation of stain release on cotton fabrics. Digitized color images of the stained fabrics are obtained, and the pixel values in the color and intensity planes of these images are probabilistically modeled as a Gaussian Mixture Model (GMM). Stain detection is posed as a decision theoretic problem, where the null hypothesis corresponds to absence of a stain. The null hypothesis and the alternate hypothesis mathematically translate into a first order GMM and a second order GMM respectively. The parameters of the GMM are estimated using a modified Expectation-Maximization (EM) algorithm. Minimum Description Length (MDL) is then used as the test statistic to decide the verity of the null hypothesis. The stain is then segmented by a decision rule based on the probability map generated by the EM algorithm. The proposed approach was tested on a dataset of 48 fabric images soiled with stains of ketchup, corn oil, mustard, ragu sauce, revlon makeup and grape juice. The decision theoretic part of the algorithm produced a correct detection rate (true positive) of 93% and a false alarm rate of 5% on these set of images.

Paper Details

Date Published: 17 February 2007
PDF: 8 pages
Proc. SPIE 6503, Machine Vision Applications in Industrial Inspection XV, 650304 (17 February 2007); doi: 10.1117/12.705105
Show Author Affiliations
Arunkumar Gururajan, Texas Tech Univ. (United States)
Hamed Sari-Sarraf, Texas Tech Univ. (United States)
Eric F. Hequet, Texas Tech Univ. (United States)

Published in SPIE Proceedings Vol. 6503:
Machine Vision Applications in Industrial Inspection XV
Fabrice Meriaudeau; Kurt S. Niel, Editor(s)

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