Machine Vision, Pattern Recognition

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

[+] Author Affiliations
Arunkumar Gururajan

Texas Tech University, Department of Electrical and Computer Engineering, Lubbock, Texas 79403

Hamed Sari-Sarraf

Texas Tech University, Department of Electrical and Computer Engineering, Lubbock, Texas 79403

Eric F. Hequet

Texas Tech University, International Textile Center, Lubbock, Texas 79403

Opt. Eng. 47(2), 027202 (February 28, 2008). doi:10.1117/1.2868783
History: Received July 07, 2007; Revised October 04, 2007; Accepted October 08, 2007; Published February 28, 2008
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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.

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© 2008 Society of Photo-Optical Instrumentation Engineers

Citation

Arunkumar Gururajan ; Hamed Sari-Sarraf and Eric F. Hequet
"Statistical approach to unsupervised defect detection and multiscale localization in two-texture images", Opt. Eng. 47(2), 027202 (February 28, 2008). ; http://dx.doi.org/10.1117/1.2868783


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