Segmentation of Images Having Unimodal Distributions

Authors:
Bir Bhanu;Olivier D. Faugeras
Affiliations:
Image Processing Institute and Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90007/ Aeronutronic Division, Ford Aerospace and Communicati;Image Processing Institute, Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90007/ INRIA, Rocquencourt, France/ University of Paris XI, Par
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1982

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Abstract

A gradient relaxation method based on maximizing a criterion function is studied and compared to the nonlinear probabilistic relaxation method for the purpose of segmentation of images having unimodal distributions. Although both methods provide comparable segmentation results, the gradient method has the additional advantage of providing control over the relaxation process by choosing three parameters which can be tuned to obtain the desired segmentation results at a faster rate. Examples are given on two different types of scenes.