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Optical Engineering

Statistical approach to unsupervised defect detection and multiscale localization in two-texture images
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Paper Abstract

We present a novel statistical approach to unsupervised detection and localization of a chromatic defect in a uniformly textured background. The test images are probabilistically modeled using Gaussian mixture models, and consequently defect detection is posed as a model-order selection problem. The statistical model is estimated using a modified Expectation-Maximization algorithm that aids in faster convergence of the scheme. A test image is segmented only if a defective region/blob has been declared to be present, and this improves the efficiency of the entire scheme. This work places equal emphasis on defect localization; hence, an elaborate statistical multiscale analysis is performed to accurately localize the defect in the image. The underlying idea behind the multiscale approach is that segmented structures should be stable across a wide range of scales. The efficacy of the proposed approach is successfully demonstrated on a large dataset of stained fabric images. The overall detection rate of the system is found to be 92% with a specificity of 95%. All of these factors make the proposed approach attractive for implementation in online industrial applications.

Paper Details

Date Published: 1 February 2008
PDF: 10 pages
Opt. Eng. 47(2) 027202 doi: 10.1117/1.2868783
Published in: Optical Engineering Volume 47, Issue 2
Show Author Affiliations
Arunkumar Gururajan, Texas Tech Univ. (United States)
Hamed Sari-Sarraf, Texas Tech Univ. (United States)
Eric Francois Hequet, Texas Tech Univ. (United States)

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