Image segmentation by edge pixel classification with maximum entropy

C. F. Sin, Chi Kin Leung

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)

Abstract

Image segmentation is a process to classify image pixels into different classes according to some pre-defined criterion. In this paper, 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 a way 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.
Original languageEnglish
Title of host publicationProceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
Pages283-286
Number of pages4
Publication statusPublished - 1 Dec 2001
Event2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001 - Hong Kong, Hong Kong
Duration: 2 May 20014 May 2001

Conference

Conference2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, ISIMP 2001
CountryHong Kong
CityHong Kong
Period2/05/014/05/01

Keywords

  • Maximum entropy
  • Segmentation
  • Template matching
  • Thresholding

ASJC Scopus subject areas

  • Computer Science(all)

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