【作者】 刘晓龙；

【导师】 张佑生；

【作者基本信息】 合肥工业大学 ， 计算机软件与理论， 2006， 硕士

【摘要】 图像分割是从输入图像中提取目标或感兴趣区域的过程,是目标检测和识别过程中的重要步骤。模糊聚类是模糊理论的一个重要的分支,在图像分割中得到广泛应用。本文对应用模糊聚类的图像分割方法进行了探讨,主要研究内容如下: 1)对模糊理论的基本内容进行了系统的介绍,并详细介绍了模糊C-均值(FCM)聚类方法和基于灰度直方图的快速FCM聚类的图像分割算法。 2)本文给出一种将模拟退火(SA)与FCM聚类相结合的算法,可以减少初始聚类中心和隶属度矩阵的选取对算法收敛性的影响。在合理选择冷却进度表的基础上,依据FCM聚类算法建立目标函数,实现了基于SA和FCM聚类的图像分割算法。 3)将核方法的思想推广到FCM聚类,构造了基于Mercer核函数的模糊核C-均值图像分割算法。通过利用Mercer核,将样本从输入空间映射到高维特征空间,使原来没有显现的特征突现出来,取得了很好的图像分割效果。更多还原

【Abstract】 Image segmentation is the process of detecting objects or interesting areas from input image, and it is an important step in object detection and recognition. Fuzzy clustering is an important branch of fuzzy set theory, and is widely applied in image segmentation. In this dissertation, the application of fuzzy clustering in image segmentation is studied. The main work of this dissertation is summarized as follows:1) The dissertation systematically introduces the fundamental knowledge of fuzzy set theory, fuzzy clustering algorithms and its application in image segmentation— fast fuzzy c-means clustering algorithm based on histogram.2) A fuzzy clustering algorithm is given which combines simulated annealing (SA) and fuzzy c-means (FCM) clustering. This algorithm can reduce the influence of selection of the initial clustering centers value and the membership matrix’ elements on the algorithm convergence. Based on choosing reasonable cooling schedule, the objection function for SA is set up according to FCM clustering, and the image segmentation algorithm based on SA and FCM clustering is implemented.3) Based on kernel method, an image segmentation algorithm of fuzzy kernel c-means is proposed. By using Mercer kernel to map the samples from original space to a high-dimension feature space, we can get good results of clustering.更多还原