Issue No. 01 - January (1982 vol. 4)
Philip A. Dondes , Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
Azriel Rosenfeld , Computer Vision Laboratory, Computer Science Center, University of Maryland, College Park, MD 20742.
An image can be segmented by classifying its pixels using local properties as features. Two intuitively useful properties are the gray level of the pixel and the ``busyness,'' or gray level fluctuation, measured in its neighborhood. Busyness values tend to be highly vari-able in busy regions; but great improvements in classification accuracy can be obtained by smoothing these values prior to classifying. An alternative possibility is to classify probabilistically and use relaxation to adjust the probabilities.
P. A. Dondes and A. Rosenfeld, "Pixel Classification Based on Gray Level and Local ``Busyness''," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 4, no. , pp. 79-84, 1982.