IMAGE SEGMENTATION USING JENSEN-SHANNON DIVERGENCE AND JENSEN-RENYI DIVERGENCE

摘要

A method of approximating the boundary of an object in an image, the image being represented by a data set, the data set comprising a plurality of data elements, each data element having a data value corresponding to a feature of the image, the method comprising determining which one of a plurality of contours most closely matches the object boundary at least partially according to a divergence value for each contour, the divergence value being selected from the group consisting of Jensen-Shannon divergence and Jensen-Renyi divergence. Each contour Ci defines a zone ZIi and a zone ZOi, ZIi representing the data elements inside the contour and ZOi representing the data elements outside the contour, each zone having a corresponding probability distribution of data values for the data elements therein, and wherein the divergence value for each contour Ci represents a measure of the difference between the probability distributions for the zones ZIi and ZOi. The boundary estimate is preferably a parametric contour. Further, the present invention supports the segmentation of multiple objects in a single data set simultaneously.