检索结果-内蒙古大学图书馆

A multilevel image thresholding using the honey bee mating optimization

APPLIED MATHEMATICS AND COMPUTATION 2010年第9期215卷 3302-3310页

作者： Horng, Ming-Huwi Natl PingTung Inst Commerce Dept Comp Sci & Informat Engn Pingtung Taiwan

Image thresholding is an important technique for image processing and pattern recognition. Many thresholding techniques have been proposed in the literature. Among them, the maximum entropy thresholding (MET) has been widely applied. In this paper, a new multilevel MET algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. This proposed method is called the maximum entropy based honey bee mating optimization thresholding (MEHBMOT) method. Three different methods such as the particle swarm optimization (pso), the hybrid cooperative-comprehensive learning based pso algorithm (HCOCLpso) and the Fast Otsu's method are also implemented for comparison with the results of the proposed method. The experimental results manifest that the proposed MEHBMOT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. In comparison with the other three thresholding methods, the segmentation results using the MEHBMOT algorithm is the best and its computation time is relatively low. Furthermore, the convergence of the MEHBMOT algorithm can rapidly achieve and the results validate that the proposed MEHBMOT algorithm is efficient. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Image thresholding Particle swarm optimization Honey bee mating optimization hybrid cooperative-comprehensive learning based pso algorithm Fast Otsu's method

来源：评论

学校读者我要写书评

暂无评论

Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation

引用

EXPERT SYSTEMS WITH APPLICATIONS 2011年第11期38卷 13785-13791页

作者： Horng, Ming-Huwi Natl PingTung Inst Commerce Dept Comp Sci & Informat Engn Pingtung City 900 Taiwan

Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (pso), the hybrid cooperative-comprehensive learning based pso algorithm (HCOCLpso), the Fast Otsu's method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Particle swarm optimization Honey bee mating optimization hybrid cooperative-comprehensive learning based pso algorithm Fast Otsu's method Artificial bee colony algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-level Thresholding Selection by using the Honey Bee Mating Optimization

Multi-level Thresholding Selection by using the Honey Bee Ma...

引用

9th International Conference on hybrid Intelligent Systems (HIS 2009)

作者： Liou, Ren-Jean Horng, Ming-Huwi Jiang, Ting-Wei Natl Pingtung Inst Commerce Dept Comp Sci & Informat Engn Pingtung Taiwan

ISBN: (纸本)9780769537450

Image thresholding is an important technique for image processing and pattern recognition. In this paper, a new multilevel image thresholding algorithm based on the technology of the honey bee mating optimization (HBMO) is proposed. Three different methods such as the particle swarm optimization (pso), the hybrid cooperative-comprehensive learning based pso algorithm (HCOCLpso) and the Fast Otsu's method are also implemented for comparison with the results of the proposed method. The experimental results reveal two important interested results for other three image thresholding methods. One is that the results of pso and Fast Ostu's method are unstable that extraordinary segmentations are generated. Another is that the results of HCOCLpso are superior to original pso method, but it still slower than ones of HBMO and it had similar segmentation results with the ones of the honey bee mating optimization.

关键词： Image thresholding particle swarm optimization honey bee mating optimization hybrid cooperative-comprehensive learning based pso algorithm Ostu's method

来源：评论

学校读者我要写书评

暂无评论

Multi-level Thresholding Selection by using the Honey Bee Mating Optimization

Multi-level Thresholding Selection by using the Honey Bee Ma...

引用

2009 Ninth International Conference on hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

作者： Ren-Jean Liou Ming-Huwi Horng Ting-Wei Jiang Department of Computer Science and Information Engineering National Pingtung Institute of Commerce 51 Min Sheng E. Road Pingtung Taiwan ROC

关键词： Image thresholding particle swarm optimization honey bee mating optimization hybrid cooperative-comprehensive learning based pso algorithm Ostu's method

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：