Pixel Classification Based on Gray Level and Local ``Busyness''

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.

[1]  Azriel Rosenfeld,et al.  Some experiments in image segmentation by clustering of local feature values , 1979, Pattern Recognit..

[2]  Azriel Rosenfeld,et al.  Neighbor gray levels as features in pixel classification , 1980, Pattern Recognit..

[3]  G.B. Coleman,et al.  Image segmentation by clustering , 1979, Proceedings of the IEEE.

[4]  Shmuel Peleg,et al.  Determining Compatibility Coefficients for Curve Enhancement Relaxation Processes , 1978 .

[5]  Azriel Rosenfeld,et al.  Scene Labeling by Relaxation Operations , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Azriel Rosenfeld,et al.  A Relaxation Method for Multispectral Pixel Classification , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.