Abstract
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.
Original language | English |
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Pages (from-to) | 339-346 |
Number of pages | 8 |
Journal | International Journal of Imaging Systems and Technology |
Volume | 10 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2 Jul 1999 |
Externally published | Yes |