【作者】 田巧玉；

【导师】 何传江；

【作者基本信息】 重庆大学 ， 应用数学， 2008， 硕士

【摘要】 图像分割和边界提取对于图像理解、模式识别、计算机视觉等具有非常重要的意义,是进一步图像分析的基础。目前,在图像分割领域,并没有对各类图像都适用的通用方法。如何快速且准确地检测到图像中目标物体或者感兴趣部分的边界,一直是人们研究的热点问题。近年来,基于偏微分方程的图像分割的研究十分活跃,成为图像分割研究领域一项受到广泛关注的技术。本学位论文首先介绍了现有的图像分割方法,以及图像分割中的偏微分方程方法,然后针对几何活动轮廓模型(GAC)进行了研究,得到如下的结果:在基于偏微分方程的图像分割中,基于边界的GAC模型几乎都依赖于停止速度函数(HSF),该函数通常是基于图像梯度定义的,其作用是使活动轮廓(演化曲线)停止在所希望的目标边界上。然而,在GAC模型中,由于传统的HSF在同质区域不够大,导致活动轮廓线不能均匀快速的演化到希望的目标边界,故基于GAC模型的图像分割有演化时间长的缺点。为了加快活动轮廓的演化速度,本文提出对HSF进行尺度变换的方法,实验结果显示,本学位论文提出的方案能够大大减少分割时间,同时,对于凹陷边界和弱边界的分割取得了更好的效果。针对无须重新初始化水平集方法(LX模型)对二值图像分割时间长的问题,本学位论文提出一种新的模型,意在分割二值图像时,可以自适应地确定曲线的演化方向,大大缩短二值图像的分割时间。实验结果表明,该算法可以大大缩短图像分割时间。更多还原

【Abstract】 Image segmentation and boundary extraction are very important in the fields of image understanding, pattern recognition, computer vision and so on. They are also the basis of the later image analysis. Up to now, there is still not a common method for any type of images in image segmentation. There are great deal of researches on how to detect the objects in an image quickly and accurately. Recently, image segmentations based on partial differential equation (PDE) has been studied broadly and become a key technique in the field of image processing.After reviewing the literatures involved image segmentation techniques and PDE–based segmentation methods, this dissertation discusses geometric active contour(GAC) models (implemented via level set methods) and obtains the following results:In PDE-based image segmentations, edge-based GAC models rely on the halting speed function (HSF), which is typically the function of image gradient, to stop the active contour (evolving curve) on the edges of the desired objects. However, the GAC models with original HSF are not able to make the active contour quickly move towards the desired object edges because the HSF is not large enough in homogenous region. Therefore, they have the drawbacks of long evolving time. In order to speed up the evolution of the active contour, this dissertation proposes a scheme that the scale transform is applied to the HSF. Experimental results show that the proposed scheme can significantly reduce segmentation time and perform better in the presence of concave and weak edges.Then, level set method without reinitialization proposed by Li etc [Level set evolution without re-initialization: a new variational formulation. IEEE International Conference on Computer Vision and Pattern Recognition, 2005] is discussed. It has also the drawback of long evolving time. In order to make the curve precisely converge to the object boundary and shorten the segmentation time, this dissertation proposes a new model for binary image segmentations. Experimental results on binary images show the proposed model perform well and greatly reduce the segmentation time.更多还原