【作者】 李率杰；

【导师】 宋锦萍； 郭柏灵；

【作者基本信息】 河南大学 ， 应用数学， 2008， 硕士

【摘要】 近年来基于水平集的图像分割方法受到了越来越多的重视,相比于传统的图像分割方法,该方法具有对初始轮廓线位置不敏感,拓扑适应性强等优点。本文首先对图像分割的目的、意义进行了简单的概述。并对Mumford-Shah模型、Chan-Vese模型以及J.Lie、M.Lysaker和X-C Tai提出的一种逐段常数水平集方法进行了介绍和分析。其次,在文献[10]基于Mumford-Shah模型的逐段常数水平集分割方法的基础上,为了提高图像分割效果和运算速度,分析并改进了惩罚项结构,提出了一种IMS(improved Mumford-Shah)分割模型。实验表明模型是可行有效的。再次,为了消除纹理或噪声对图像分割的影响,在Mumford-Shah模型中引入了图像分解的思想,并结合逐段常数水平集方法,提出了MSWD(Mumford-Shah withDecomposition)分割模型,能够分离出图像中的纹理或噪声,避免了对分割结果带来的影响,且给出了详尽的运算过程,并通过仿真实验证明了模型的有效性和实用性。最后针对脉冲噪声的特点给出了一种有效的迭代滤波算法。更多还原

【Abstract】 Recently,a segmentation methods based on level set have attracted more and moreattentions. Compared to traditional image segmentation approaches, the method havemany prominent virtues: such as insensitivity to the initial curve position, the strongability to deal with the topological changes etc.First of all, the paper summarizes the purpose and significance of image segmentation,introduce and analyze Mumford-Shah model,Chan-Vese model and piecewise constant levelset method.Second, in the base of piecewise constant level set image segmentation method basedon Mumford-Shah model in [10], in order to improve the segmentation e?ect and increasethe algorithm speed, we analyze and improve structure of the term ,we propose a improvedMumford-shah model.The experiment shows the model is validity.Third, in order to avoid the in?uence of texture, we adopt decomposition idea inMumford-Shah model, and combine with piecewise constant level set method. We proposea MSWD(Mumford-Shah with decomposition)model. the model can separate texture ornoise. We also show the algorithm methods, and testify the rationality and validity of themodel by experiment.Finally, we propose an iterative filter algorithm based on impulse noise.更多还原