摘要视觉是人类最重要的感知手段，图像又是视觉的基础，所以在计算机视觉领域中图像分割得到越来越多的重视。本文以数学形态学为基础研究了图像分割算法，故首先在文章中介绍了数学形态学的相关知识。然后，我们分别对基于形态学的边缘检测和基于形态学的区域分割进行讨论。64572

分水岭是运用数学形态学思想的一种实用性非常广泛的图像分割工具。本文将围绕分水岭变换方面，分析分水岭变换的优缺点，针对传统的分水岭方法存在着严重的过分割现象，分析“过分割”所产生的原因。利用形态学结构元素，设计多形状多尺度的结构元复合滤波器，过滤图像中的噪声，保留完整的图像细节信息，再对梯度图像进行传统的分水岭变换，相当于在进行分水岭变换之前进行预处理工作。实验结果证明，该方法能有效抑制自然背景，并提取出人造目标。

毕业论文关键词  图像分割，数学形态学，边缘检测，分水岭变换

毕业设计说明书（论文）外文摘要

Title The  research  of image segmentation based on mathematical morphology 

Abstract

Vision is the most important sense of human perception .Image is the basis of vision.So in the field of computer vision, image segmentation has been attached more and more importance .In this paper，image processing algorithms are researched based on mathematical morphology.so，in this paper， we introduce origin of mathematics morphology from binary morphology to gray morphology and extensively study its different operators and quality. Then the image segmentation based on edge detection with morphology and region segmentation with morphology  are expounded in detail.

Watershed is a generally used image segmentation tool recent years using the idea of mathematical morphology. This paper discusses researches the merit and demerit of watershed method when used in image segmentation， discusses the appearance of over.segmentation， and analyzes the reason of the over.segmentation. Specific to over.segmentation of the traditional method， this paper designs an multi.shape multi.scale structure compound filter using morphological structure elements to filter the noisy in images and retain unbroken detail information of images. Then use the traditional watershed method on the gradient images， as a preprocess step before watershed transform. The experiment results show that this algorithm can extract the target from natural background effectively ，and keep complete contour of the artificial target as well.

Key words：image segmentation， mathematical morphology，edge detection，Watershed transform

目次

1  绪论 1

1.1  背景和意义 1

1.2  国内外研究现状 2

1.3  本文的主要工作 2

2  数学形态学基本理论 3

2.1  数学形态学的发展历史 3

2.2  二值膨胀和腐蚀 4

2.3  灰度形态学 6

3  图像分割 8

3.1  图像分割的定义 8

3.2  基于边缘的分割技术 8

3.3基于区域特征的分割 12

3.4基于阈值的分割技术 15

4  分水岭分割 17

4.1  分水岭分割介绍