Thresholding Using Relaxation

Authors:
Azriel Rosenfeld;Russell C. Smith
Affiliations:
Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.;Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1981

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Abstract

If a picture contains dark objects on a light background (or vice versa), the objects can be extracted by thresholding, i.e., by classifying the pixels into ``light'' and ``dark'' classes. If the picture is noisy, so that the object and background gray level populations overlap, there will be errors in the thresholded output. A relaxation process can be used to reduce these errors; we classify the pixels probabilistically, and then adjust the probabilities for each pixel, based on its neighbors' probabilities, with light reinforcing light and dark dark. When this adjustment process is iterated, the dark probabilities become very high for pixels that belong to dark regions, and vice versa, so that thresholding becomes trivial.