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Title: Image segmentation by edge pixel classification with maximum entropy
Authors: Sin, CF
Leung, CK
Keywords: Image classification
Image segmentation
Maximum entropy methods
Issue Date: 2001
Source: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001, 02 May 2001-04 May 2001, Hong Kong, p. 283-286 How to cite?
Abstract: Image segmentation is a process to classify image pixels into different classes according to some pre-defined criterion. An entropy based image segmentation method is proposed to segment a gray-scale image. The method starts with an arbitrary template. An index called Gray-scale Image Entropy (GIE) is employed to measure the degree of resemblance between the template and the true scene that gives rise to the gray-scale image. The classification status of the edge pixels in the template is modified in such a way as to maximize the GIE. By repeatedly processing all the edge pixels until a termination condition is met, the template would be changed to a configuration that closely resembles the true scene. This optimum template (in an entropy sense) is taken to be the desired segmented image. Investigation results from simulation study and the segmentation of practical images demonstrate the feasibility of the proposed method
ISBN: 962-85766-2-3
DOI: 10.1109/ISIMP.2001.925389
Appears in Collections:Conference Paper

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