Linear flaw detection in woven textiles using model-based clustering
We combine image-processing techniques with a powerful new statistical technique to detect linear pattern production faults in woven textiles. Our approach detects a linear pattern in preprocessed images via model-based clustering. It employs an approximate Bayes factor which provides a criterion for assessing the evidence for the presence of a defect. The model used in experimentation is a (possibly highly elliptical) Gaussian cloud superimposed on Poisson clutter. Results are shown for some representative examples, and contrasted with a Hough transform. Software for the statistical modeling is available.
model-based clustering hough transform pattern recognition bayesian cluster analysis machine vision industrial inspection