1 February 2008 Statistical approach to unsupervised defect detection and multiscale localization in two-texture images
Author Affiliations +
Optical Engineering, 47(2), 027202 (2008). doi:10.1117/1.2868783
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.
Arunkumar Gururajan, Hamed Sari-Sarraf, Eric Francois Hequet, "Statistical approach to unsupervised defect detection and multiscale localization in two-texture images," Optical Engineering 47(2), 027202 (1 February 2008). http://dx.doi.org/10.1117/1.2868783
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KEYWORDS
Image segmentation

Expectation maximization algorithms

Defect detection

Statistical analysis

Data modeling

Colorimetry

Image processing algorithms and systems

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