基于模糊理论分析的图像分割算法分析-analysis of image segmentation algorithm based on fuzzy theory analysis.docx

摘 要I摘 要图像分割是一种基本的计算机视觉技术，是由图像处理到图像分析的关键步 骤。由于成像过程中受各种因素的影响，图像自身存在着许多不确定性和不精确 性的问题。而模糊理论方法对于图像的这种不确定性有很好的描述能力，因而近 年来基于模糊方法的图像分割算法得到了广泛的应用与研究。本论文主要是在现 存的模糊聚类算法和模糊轮廓模型算法的基础上，着重分析了目前基于模糊的分 割算法所存在的问题，并通过深入研究获得了有效的方法来解决模糊方法应用于 图像分割所面临的一些实际问题。本文的主要工作如下：1. 针对于含有高强度噪声图像的分割区域一致性不好与分割精度低的问题， 本文提出了一种新的基于空间邻域信息的模糊 C 均值图像分割算法。该算法引入 了加权平衡因子，考虑了像素点的空间距离约束和空间灰度约束，保证了空间邻 域信息的完整性。其次，改进算法还采用核空间距离测度进行聚类，准确反映出 了数据的分布特点，提高了分割的精度。此外，改进算法避免了参数选择的问题， 提升了聚类算法的稳定性和聚类性能。实验结果表明，该算法得到的图像聚类分 割结果更稳定、边缘更平滑、区域一致性更好，具有很强的噪声抑制能力。2. 针对于灰度不均匀图像的分割问题，本文提出一种基于邻域信息的模糊活 动轮廓模型用于灰度不均匀图像分割。由于现存的模糊活动轮廓模型仅利用图像 的全局信息，因此改进模型算法通过引入高斯核函数来控制邻域窗口的作用范围， 充分利用灰度不均匀图像的局部统计信息。经实验证明，用改进的模糊活动轮廓 模型对灰度不均匀图像进行分割，能获得清晰、准确的分割结果，提高图像分割 的准确性和效率。关键词：模糊聚类图像分割空间约束 核函数 模糊活动轮廓AbstractIIIAbstractImage segmentation is a basic technology of computer vision, which is the key step from image processing to image analysis. During imaging, due to the influences of all kinds of factors, the image exist many uncertain and inaccurate factors. However, the fuzzy theory can well describe these uncertainties of the image. In recent years, image segmentation based on fuzzy theory has been widely studied. This thesis mainly overviewed the classical fuzzy clustering algorithm and the fuzzy active contour, and analyzes the existing problems of image segmentation algorithm based on fuzzy thoery. And an in-depth study on this problem is done in this thesis. The new effective methods have been designed for some actual problems of the fuzzy clustering applied to image segmentation.The main works in this thesis as follow:For the problems of the bad segmentation region consistency and the segmentation of the low accuracy in the image containing high strength noise, we proposes a new fuzzy c-means clustering algorithm of image segmentation based on neighborhood spatial information in this paper. The proposed algorithm introduced the trade-off weighted fuzzy factor, considering pixels space distance constraints and spatial gray level constraints. Simultaneously