Addressing the problem of automatic fault detection in woven and dyed fabric, we discuss a number of new statistical model-based methods and relate them to a first stage of point/local detection and a second stage of extended pattern detection. One model-based method defines a maximum likelihood binarization of the image. In another model-based method, we describe a discrete Fourier transform-based texture analysis technique that is highly effective for woven textiles in discriminating subtle flaw patterns from the pronounced background of repetitive weaving pattern and random clutter. Finally, we describe a model-based clustering method that can be employed to aggregate perceptual groupings of point and local detections.
|Number of pages
|International Journal of Imaging Systems and Technology
|Published - 2 Jul 1999