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Model‐based methods for textile fault detection

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. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 339–346, 1999