A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation

被引：182

作者：

Hammouche, Kamal ^{[2
]}

Diaf, Moussa ^{[2
]}

Siarry, Patrick ^{[1
]}

机构：

[1] Univ Paris 12, Lab Images Signaux & Syst Intelligents LiSSi, EA 3956, F-94010 Creteil, France

[2] Univ Mouloud Mammeri, Dept Automat, Tizi Ouzou, Algeria

来源：

COMPUTER VISION AND IMAGE UNDERSTANDING | 2008年 / 109卷 / 02期

关键词：

thresholding; image segmentation; genetic algorithm;

D O I：

10.1016/j.cviu.2007.09.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a multilevel thresholding method which allows the determination of the appropriate number of thresholds as well as the adequate threshold values is proposed. This method combines a genetic algorithm with a wavelet transform. First, the length of the original histogram is reduced by using the wavelet transform. Based on this lower resolution version of the histogram, the number of thresholds and the threshold values are determined by using a genetic algorithm. The thresholds are then projected onto the original space. In this step, a refinement procedure may be added to detect accurate threshold values. Experiments and comparative results with multilevel thresholding methods over a synthetic histogram and real images show the efficiency of the proposed method. (C) 2007 Elsevier Inc. All rights reserved.

引用

页码：163 / 175

页数：13

共 30 条

[1] Image segmentation by histogram thresholding using hierarchical cluster analysis [J].

Arifin, Agus Zainal ;

Asano, Akira .

PATTERN RECOGNITION LETTERS, 2006, 27 (13) :1515-1521

[2] Image thresholding based on the EM algorithm and the generalized Gaussian distribution [J].

Bazi, Yakoub ;

Bruzzone, Lorenzo ;

Melgani, Farid .

PATTERN RECOGNITION, 2007, 40 (02) :619-634

[3]

CHANG Y, 2003, C RES PRACTICE INFOR, V22

[4]

Chong Jinsong, 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293), P1247, DOI 10.1109/IGARSS.1999.774593

[5] A multi-level thresholding approach using a hybrid optimal estimation algorithm [J].

Fan, Shu-Kai S. ;

Lin, Yen .

PATTERN RECOGNITION LETTERS, 2007, 28 (05) :662-669

[6]

Goldberg D.E, 1989, GENETIC ALGORITHMS S

[7] A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis [J].

Gonzales-Barron, U ;

Butler, F .

JOURNAL OF FOOD ENGINEERING, 2006, 74 (02) :268-278

[8] On minimum variance thresholding [J].

Hou, Z. ;

Hu, Q. ;

Nowinski, W. L. .

PATTERN RECOGNITION LETTERS, 2006, 27 (14) :1732-1743

[9] A NEW METHOD FOR GRAY-LEVEL PICTURE THRESHOLDING USING THE ENTROPY OF THE HISTOGRAM [J].

KAPUR, JN ;

SAHOO, PK ;

WONG, AKC .

COMPUTER VISION GRAPHICS AND IMAGE PROCESSING, 1985, 29 (03) :273-285

[10] Fast image segmentation based on multi-resolution analysis and wavelets [J].

Kim, BG ;

Shim, JI ;

Park, DJ .

PATTERN RECOGNITION LETTERS, 2003, 24 (16) :2995-3006

← 1 2 3 →