Hierarchical genetic image segmentation algorithm based on histogram dichotomy

Hanchuan Peng; Fuhui Long; Zheru Chi; David Dagan Feng; Wanchi Siu

doi:10.1049/el:20000660

Hierarchical genetic image segmentation algorithm based on histogram dichotomy

Hanchuan Peng, Fuhui Long, Zheru Chi, David Dagan Feng, Wanchi Siu

Research output: Journal article publication › Journal article › Academic research › peer-review

4 Citations (Scopus)

Abstract

A new hierarchical distributed genetic algorithm (HDGA) for image segmentation is proposed based on histogram dichotomy without an a priori number image regions. It can take advantage of statistical information and spatial information to segment an image. Experimental results show that HDGA outperforms the distributed genetic algorithm (DGA) in both computational efficiency and segmentation quality.

Original language	English
Pages (from-to)	872-874
Number of pages	3
Journal	Electronics Letters
Volume	36
Issue number	10
DOIs	https://doi.org/10.1049/el:20000660
Publication status	Published - 11 May 2000

ASJC Scopus subject areas

Electrical and Electronic Engineering

More information

10.1049/el:20000660

Scopus Link

Cite this

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title = "Hierarchical genetic image segmentation algorithm based on histogram dichotomy",

abstract = "A new hierarchical distributed genetic algorithm (HDGA) for image segmentation is proposed based on histogram dichotomy without an a priori number image regions. It can take advantage of statistical information and spatial information to segment an image. Experimental results show that HDGA outperforms the distributed genetic algorithm (DGA) in both computational efficiency and segmentation quality.",

author = "Hanchuan Peng and Fuhui Long and Zheru Chi and Feng, {David Dagan} and Wanchi Siu",

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AU - Peng, Hanchuan

AU - Long, Fuhui

AU - Chi, Zheru

AU - Feng, David Dagan

AU - Siu, Wanchi

PY - 2000/5/11

Y1 - 2000/5/11

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AB - A new hierarchical distributed genetic algorithm (HDGA) for image segmentation is proposed based on histogram dichotomy without an a priori number image regions. It can take advantage of statistical information and spatial information to segment an image. Experimental results show that HDGA outperforms the distributed genetic algorithm (DGA) in both computational efficiency and segmentation quality.

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JF - Electronics Letters

SN - 0013-5194

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