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

Arunkumar Gururajan, Hamed Sari-Sarraf, Eric F. Hequet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Machine Vision Applications in Industrial Inspection XV
PublisherSPIE
ISBN (Print)0819466166, 9780819466167
DOIs
StatePublished - 2007
EventMachine Vision Applications in Industrial Inspection XV - San Jose, CA, United States
Duration: Jan 29 2007Jan 30 2007

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6503
ISSN (Print)0277-786X

Conference

ConferenceMachine Vision Applications in Industrial Inspection XV
CountryUnited States
CitySan Jose, CA
Period01/29/0701/30/07

Keywords

  • Expectation-maximization
  • Gaussian mixture model
  • Minimum description length

Fingerprint Dive into the research topics of 'Statistical modeling, detection and segmentation of stains in digitized fabric images'. Together they form a unique fingerprint.

Cite this