Performance analysis for a class of iterative image thresholding algorithms

Chi Kin Leung, F. K. Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

57 Citations (Scopus)

Abstract

A performance analysis procedure that analyses the properties of a class of iterative image thresholding algorithms is described. The image under consideration is modeled as consisting of two maximum-entropy primary images, each of which has a quasi-Gaussian probability density function. Three iterative thresholding algorithms identified to share a common iteration architecture are employed for thresholding 4595 synthetic images and 24 practical images. The average performance characteristics including accuracy, stability, speed and consistency are analysed and compared among the algorithms. Both analysis and practical thresholding results are presented.
Original languageEnglish
Pages (from-to)1523-1530
Number of pages8
JournalPattern Recognition
Volume29
Issue number9
DOIs
Publication statusPublished - 1 Sep 1996

Keywords

  • Image segmentation
  • Image thresholding
  • Iterative algorithm
  • Maximum entropy
  • Performance analysis

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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