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A statistical approach to defect detection and discrimination has been applied to the case of hot rolled steel. The
probability distribution of pixel intensities has been estimated from a small set of images without defects, and
this distribution is used to select pixels with unlikely values as candidates for defects. Discrimination of true
defects from random noise pixels is achieved by a dynamical thresholding procedure, which tracks the behaviour
of clusters of selected pixels for varying threshold level.