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

Abstract

We present a novel statistical approach to unsupervised de- tection and localization of a chromatic defect in a uniformly textured background. The test images are probabilistically modeled using Gauss- ian 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 conver- gence of the scheme. A test image is segmented only if a defective region/blob has been declared to be present, and this improves the effi- ciency of the entire scheme. This work places equal emphasis on defect localization; hence, an elaborate statistical multiscale analysis is per- formed 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.