【作者】 胡阳涟；

【导师】 赵凤群；

【作者基本信息】 西安理工大学 ， 计算数学， 2008， 硕士

【摘要】 图像分割是计算机视觉中的关键技术之一。基于马尔可夫随机场(Markov RandomField,MRF)模型的图像分割方法,是一种基于统计的分割方法,具有能充分利用先验知识,能形成闭合的边界,模型参数少且易于和其他方法相结合等优点,所以此方法在图像分割领域中得到了广泛的应用。本文研究了基于MRF的图像分割算法,重点研究了基于MRF的图像分割模型中的参数估计方法,以及MRF中的最大后验概率(Maximum A Posteriori,MAP)问题的求解方法。首先,研究了MRF中MAP问题的求解方法。为提高传统的模拟退火(SimulatedAnnealing,SA)算法求解MAP问题的速度,在SA算法基础上提出了一种基于振动点的SA算法。在初始分割后,将图像的像素点分为两类:振动点和稳定点,并借助链表的数据结构存储振动点,每次迭代只对链表里面的振动点进行计算,以减少运算量。另外,本文还对SA算法的停步准则进行了改进,避免了全局能量的计算。实验表明这种基于振动点的改进SA算法在不影响分割效果的前提下,大幅度提高了计算效率。其次,研究了MRF中的参数估计方法。介绍了两种传统的参数估计方法:样本训练法和EM算法,并对两种方法进行了数值模拟和对比。然后,结合四叉树分解提出了一种新的非均匀MRF的耦合系数估计方法。实验表明,本文的估计方法较为准确,将它应用到图像分割中,能增强图像分割的自适应性,改善分割效果。更多还原

【Abstract】 Image segmentation is one of key issues in computer vision. The image segmentation based on MRF model has received much appreciation, such as the ability to make use of prior knowledge, the ability to generate connected boundary, less parameter, can easily combine with other segmentation method. So the algorithm has widely used in the image segmentation field.Image segmentation algorithm based on MRF is studied in this thesis, and especially two problems are discussed, that is the parameter estimation in MRF and the solving of the MAP problem in MRF.Firstly, the MAP problem in MRF is discussed. A new SA algorithm based on vibrant points is presented to increase the speed of the traditional SA algorithm in solving the MAP problem. After the pre-segmentation of the image, image pixels are divided into two classes: the stable points and the vibrant points. The vibrant points are stored by a linked list. Only the vibrant points are dealt with in each iteration to reduce computation load. And then, the stop rule of the SA algorithm is also improved to avoid the computation of global energy. The experiment results indicate that the improved SA algorithm based on vibrant points can greatly improve the computational efficiency while maintaining the segmentation effect.Secondly, the parameter estimation method in MRF is studied. Two traditional parameter estimation algorithms are introduced: the sample training algorithm and the EM algorithm, and then numerical experiments are done to compare with the two algorithms. Afterward, a novel estimate method based on quad-tree decomposing is proposed to estimate the isomorphic coefficients in inhomogeneous markov random field. The experiment results show that the isomorphic coefficient estimated by this algorithm can significantly improve the effect of the segmentation and the adaptability of the image segmentation algorithm.更多还原