Image segmentation based on Differential Evolution algorithm

Threshold segmentation is a critical technology of image segmentation. When the image is low signal-to-noise, the maximum between-cluster variance method (OTSU) cannot provide the ideal result. The 2D maximum between-cluster variance method can perform well with sharply increased computation. This w...

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Bibliographic Details
Published in2009 International Conference on Image Analysis and Signal Processing pp. 48 - 51
Main Authors Zhenkui Pei, Yanli Zhao, Zhen Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2009
Subjects
Chromium
Computer vision
Differential Evolution
Educational institutions
Forward contracts
Genetic algorithms
Genetic mutations
Image segmentation
Information technology
OTSU
Pattern recognition
Petroleum
threshold segmentation
threshold selection
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ISBN9781424439874
1424439876
ISSN2156-0110
DOI10.1109/IASP.2009.5054643

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Summary:Threshold segmentation is a critical technology of image segmentation. When the image is low signal-to-noise, the maximum between-cluster variance method (OTSU) cannot provide the ideal result. The 2D maximum between-cluster variance method can perform well with sharply increased computation. This work proposes a new image segmentation method based on OTSU and differential evolution. This solution performs a pre-processing step before the image segmentation. It is shown that differential evolution presents good segmentation result in noisy images. Moreover, the use of this method is easier and faster compared to the 2D maximum between-cluster variance method.
ISBN:9781424439874
1424439876
ISSN:2156-0110
DOI:10.1109/IASP.2009.5054643