Color image segmentation based on the normal distribution and the dynamic thresholding

Seon Do Kang, Hun Woo Yoo, Dong Sik Jang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


A new color image segmentation method is proposed in this paper. The proposed method is based on the human perception that in general human has attention on 3 or 4 major color objects in the image at first. Therefore, to determine the objects, three intensity distributions are constructed by sampling them randomly and sufficiently from three R, G, and B channel images. And three means are computed from three intensity distributions. Next, these steps are repeated many times to obtain three mean distribution sets. Each of these distributions comes to show normal shape based on the central limit theorem. To segment objects, each of the normal distribution is divided into 4 sections according to the standard deviation (section1 below - σ , section 2 between - σ and μ , section 3 between μ and σ , and section 4 over σ). Then sections with similar representative values are merged based on the threshold. This threshold is not chosen as constant but varies based on the difference of representative values of each section to reflect various characteristics for various images. Above merging process is iterated to reduce fine textures such as speckles remained even after the merging. Finally, segmented results of each channel images are combined to obtain a final segmentation result. The performance of the proposed method is evaluated through experiments over some images.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2007 - International Conference, Proceedings
PublisherSpringer Verlag
Number of pages13
EditionPART 1
ISBN (Print)9783540744689
Publication statusPublished - 2007
EventInternational Conference on Computational Science and its Applications, ICCSA 2007 - Kuala Lumpur, Malaysia
Duration: 2007 Aug 262007 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume4705 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Conference on Computational Science and its Applications, ICCSA 2007
CityKuala Lumpur


  • Central limit theorem
  • Dividing
  • Merging
  • Normal distribution
  • Segmentation
  • Standard deviation
  • Threshold

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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