【作者】 刘盈盈；

【导师】 石跃祥；

【作者基本信息】 湘潭大学 ， 计算机应用技术， 2008， 硕士

【摘要】 随着科学技术的进步和计算机的广泛使用,数字图像处理技术已经渗透到人类生活的各个方面,并发挥着越来越重要的作用。图像分割就是指把图像分解成各具特性的区域并提取出感兴趣目标的技术和过程,它是由图像处理到图像分析的一个关键步骤,在计算机视觉、模式识别和医学图像处理等实际中得到了广泛的应用。如何对彩色图像中的目标进行有效的分割是计算机视觉和图像分析的重点和难点,目前对彩色图像的分割方法主要分类为基于阈值的分割技术、基于边界的分割技术、基于区域特性的分割技术和结合特定理论工具的分割技术四类。本文在传统的基于聚类算法的基础上,引入图像分层的理念,提出了一种基于二叉树结构的彩色图像分割方法,分层目的是将图像在不同分辨率下由粗到细地表达出来,基于分层的图像处理方法能够充分组合利用图像的全局与局部信息、空间与灰度信息.首先,对待分割图像采用最优阈值化方法获取R,G,B三个颜色空间的最佳阈值,从而得到三个颜色空间的二值图像,然后通过构造自适用二叉树进行一次粗分割提取目标区域,实现对图像的粗分割,自适应二叉树图像分块的基本思想是先把阈值化后的二值图像作为二叉树的根节点,以二值图像像素一致为基础,采用区域距离度量的方法进行区域分裂,在计算区域的距离时,我们需要考虑两个方面;区域的颜色距离和边缘距离,从而构造图像的分裂二叉树,得到图像的颜色一致区域。通过构造二叉树进行粗分割可以把图像上颜色一致的区域初步的分割开,而并不理解其中的含义。而且这样分割的图像往往呈方形,分割的结果不满足人们的视觉感知。因此本文将在此基础上采用C-均值聚类算法实现对图像粗分割后形成的叶子区域进行聚类。从而使分割后的图像更具有现实意义。本文使用Visual C++ 6.0实现了算法,实验结果证明,本文所提出的方法与传统的区域生长方法和C-均值聚类算法的相比,可以更好的实现目标图像和背景的分离。更多还原

【Abstract】 With the development of the science and technology and widely usage of computer, technology of digital image processing have come into everyday life and have an important effect on everybody. Image segmentation is just to segment an image into different sub-images with different characters and get some interested objects. It is a key step from image process to image analysis, plays an important role in image engineering, and is applied in a lot of fields such as computer vision, pattern recognition, medical image and so on..How to segment the target of the color image is the key and difficult of compute vision and Image Analysis. In recent years,People Separate image segmentation algorithms into 4 species.they are segmentation algorithms based on threshold , algorithms based on edge detection, algorithms based on regional characteristics,and algorithms combined with specific theoty.On the basis of the the traditional clustering algorithm and the concept of tiered.an effective segmentation method based on binary tree is proposed in this paper. The aim is to express image from fine to coarse according to different resolution. Image processing based on the images layered approach can take full advantage of global and local information of the image, space and gray information. In the way of the the method used segmentation method based on binary tree, first, this method uses the optimal threshold to get the best threshold in the R.G..B color space.thus gained the binary image of R,G,B color space.Then a roughly extract of the color image is gotten by constructing the self-adapting binary tree. the basic idea of the segmentation method based on binary is we use the binary image as the Root node first, based on the consistency of the pixel. we split the picture by measure the distance of regions,there are two respect we need to consider: the color distance and the edge distance.so we can obtain the binary tree of the image and the ,construction of the images so as to split the binary tree and the color coherent region. now the image is segmented to color coherent region segment but without understanding the meaning of the image.and the image was often square which dose not meet people’s visual perception. After extracting, C_means clustering algorithm is used to improve the accuracy of the segmentation of the binary tree’s leaves. Experiments show that this method can separation of goals and background better compaare to the algorithms based on regional growth and clustering.更多还原