基于量子粒子群算法的双阈值图像分割方法

童小念; 施博; 王江晴

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基于量子粒子群算法的双阈值图像分割方法

信息工程 | 更新时间：2023-11-14

- 基于量子粒子群算法的双阈值图像分割方法
- Dual-threshold Image Segmentation Method Based on QPSO Algorithm
- 工程科学与技术 2010年42卷第3期页码：132-138
- 作者机构：
  
  中南民族大学计算机科学学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(60975021)
- DOI：
  中图分类号：
- 纸质出版日期：2010，
  
  网络出版日期：2010-3-24，
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童小念,施博,王江晴.基于量子粒子群算法的双阈值图像分割方法[J].工程科学与技术,2010,42(3):132-138.

Tong Xiaonian, Shi Bo, Wang Jiangqing. Dual-threshold Image Segmentation Method Based on QPSO Algorithm[J]. Advanced Engineering Sciences, 2010,42(3):132-138.
童小念,施博,王江晴.基于量子粒子群算法的双阈值图像分割方法[J].工程科学与技术,2010,42(3):132-138. DOI：

Tong Xiaonian, Shi Bo, Wang Jiangqing. Dual-threshold Image Segmentation Method Based on QPSO Algorithm[J]. Advanced Engineering Sciences, 2010,42(3):132-138. DOI：

摘要

中文摘要: 为了提高图像分割效率，将量子粒子群算法QPSO应用于图像阈值分割领域，并在QPSO算法基础上提出了一种基于边界控制的量子粒子群阈值分割算法BQPSO。改进算法BQPSO引入了边界控制策略，使得飞越搜索区域的粒子不再聚集到区域的边界，而是回到搜索区域内边界附近的某一位置，保持了群体的多样性，有效地避免了算法陷入局部最优解，增强了算法的全局搜索能力。实验结果表明，与遗传算法GA、粒子群算法PSO和标准量子粒子群算法QPSO的阈值寻优结果相比较，BQPSO算法在运算效率、阈值搜索精度和稳定性以及图像分割效果等方面均具有明显的优势

Abstract

Abstract:In order to improve the efficiency of image segmentation

Quantum-behaved Particle Swarm Optimization (QPSO ) algorithm was used to image threshold segmentation

and BQPSO

an improved threshold searching algorithm based on QPSO

was proposed. BQPSO algorithm introduced a boundary-controlled strategy to reset particles back to a random point around the border in search region when particles were massed on border

so to prevent them from aggregating at the border. By Boundary-controlled strategy

a diversity of the swarm was maintained

local optimal solution was avoided efficiently

and the global search ability was enhanced. The experiment result showed that

compared with GA

PSO and QPSO

BQPSO algorithm possess obviously advantage in threshold searching efficiency

searching accuracy and image segmentation effect.

关键词

量子粒子群算法遗传算法粒子群算法最大类间方差边界控制策略双阈值图像分割

Keywords

QPSOGAPSOOtsuBoundary-controlled strategyDual-threshold image segmentation

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