遗传算法新论文

学校代码 10126 学号 00708037 分类号密级

本科毕业论文

基于遗传算法的图像阈值分割

学院、系数学科学学院计算数学系

专业名称信息与计算科学

年级 2007级

学生姓名刘家祥

指导教师曹军

2011年 5月 20 日

内容摘要

图像分割就是指把图像分成各具特性的区域并提取出感兴趣目标的技术和过程。图像的分割是以灰度值作为分割的依据,通过各个像素的灰度值和事先确定的阈值的比较来分割图像。如何确定最合适的阈值是处理好图像分割的关键,这自然成为一直以来分割算法研究的焦点。

遗传算法是对生物进化论中自然选择和遗传学机理中生物进化过程的模拟来计算最优解的方法。遗传算法具有众多的优点,如鲁棒性、并行性、自适应性和快速收敛,可以应用在图像处理技术领域中图像分割技术来确定分割阈值。

本文主要介绍基于遗传算法的最小误差阈值法、最大类间方差法（Otsu法）以及最佳直方图熵法(KSW熵法)等三种方法分割图像。

关键词：图像分割，遗传算法，阈值分割

Abstract

Image segmentation refers to the image into regions each with characteristics and goals of the technology to extract and process of interest. Segmentation is a segmentation based on gray value, gray value of each pixel through the predetermined threshold value and comparing the image segmentation. How to determine the most appropriate threshold is the key to handling image segmentation, which has naturally become the focus of segmentation algorithms.

Genetic algorithm is a biological theory of evolution and genetic mechanism of natural selection in biological evolution simulation method to calculate the optimal solution. Genetic algorithm has many advantages, such as robustness, parallel, adaptive, and fast convergence, can be used in the field of image processing image segmentation technique to determine the split threshold.

In this paper, genetic algorithm based on minimum error threshold, the largest class variance (Otsu method) and the best histogram entropy (KSW entropy method) are three ways to split the image.

Keywords : Image segmentation, genetic algorithms, threshold

目录

第一章绪论 .................................................. - 1 - 第二章遗传算法概述 ........................................ . - 2 -

2.1遗传算法的研究历史....................................... - 2 -

2.2生物背景................................................. - 2 -

2.3遗传算法的基本思想....................................... - 3 -

2.4遗传算法的几个概念....................................... - 4 -

2.4.1适应度函数......................................... - 4 -

2.4.2遗传算法最常用的算子............................... - 4 -

2.5遗传算法运算的基本流程................................... - 5 - 第三章图像分割的现状 ........................................ - 7 -

3.1图像分割简介............................................. - 7 -

3.2图像分割方法............................................. - 8 -

3.2.1基于边缘检测的分割................................. - 8 -

3.2.2基于区域的分割..................................... - 8 -

3.2.3边缘与区域相结合的分割............................. - 9 -

3.3阈值选取................................................. - 9 - 第四章基于遗传算法的图像阈值分割 ........................... - 10 -

4.1图像阈值................................................ - 10 -

4.2阈值分割的原理.......................................... - 10 -

4.3最小误差阈值法.......................................... - 11 -

4.3.1最小误差法图像阈值分割............................ - 11 -

4.3.2 利用遗传算法来改进最小误差法...................... - 12 -

4.4 最大类间方差法（Otsu法）............................... - 13 -

4.4.1最大类间方差法（Otsu法）阈值分割.................. - 13 -

4.4.2 Otsu阈值分割的遗传算法设计........................ - 15 -

4.5 KSW熵法................................................ - 17 -

4.5.1 KSW熵阈值分割................................... - 17 -

4.5.2 KSW单阈值分割的遗传算法设计..................... - 18 -

4.5.3 KSW双阈值分割的遗传算法设计..................... - 19 - 第五章基于新的遗传算法的图像分割 ........................... - 25 -

5.1混沌遗传算法............................................ - 25 -

5.2量子遗传算法............................................ - 25 -

5.3免疫遗传算法............................................ - 25 - 结论 .......................................................... - 26 - 致谢 .......................................................... - 27 - 参考文献: ..................................................... - 28 -