التفاصيل البيبلوغرافية
| العنوان: |
Statistical approach to unsupervised defect detection and multiscale localization in two-texture images. |
| المؤلفون: |
Arunkumar Gururajan, Hamed Sari-Sarraf, Eric F. Hequet |
| المصدر: |
Optical Engineering; Feb2008, Vol. 47 Issue 2, p27202-27202, 1p |
| مصطلحات موضوعية: |
EXPECTATION-maximization algorithms, GAUSSIAN processes, ALGORITHMS, IMAGE analysis |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
