【作者】 夏勇；

【导师】 赵荣椿； 冯大淦；

【作者基本信息】 西北工业大学 ， 计算机科学与技术， 2006， 博士

【摘要】 纹理图像分割是数字图像处理研究的一个重要分支，是众多图像分析和机器视觉应用的基础。但是，一方面由于自然纹理类型庞杂、形态各异且结构繁复，另一方面也因为对人类视觉系统感知纹理的机理认识不足，纹理图像分割一直是图像处理领域的一大难题。在过去的四十多年中，广大研究人员虽然提出了大量的纹理图像分割算法，似是这些算法都存在着一定的不足。迄今为止，纹理图像分割仍然是一个没有得到很好解决的富有挑战性的课题。本文以灰度自然纹理图像的自动分割方法为研究内容，对目前广泛采用的一些纹理描述方法和纹理图像分割方法进行了认真的研究和总结，对各种方法的理论和实验结果进行了深入的分析和对比，选择了从基于特征的角度研究纹理图像分割问题。基于特征的纹理图像分割包括特征提取和图像分割这两个步骤。前者是描述图像的过程，旨在将图像中属于同一种纹理的像素映射为相似的矢量；后者进一步将矢量映射为类别标号，实现从特征集合到分割结果的转化。本文分别对这两个步骤进行了研究，完成了以下几个方面的工作：1、对纹理图像分割的研究意义、研究现状，特别是各类纹理图像分割方法的基本思想、算法的提出和各种改进进行了比较全面的总结，旨在通过这些总结来说明本文对纹理图像分割研究的深刻认识。2、研究了基于分形模型的纹理特征。提出了一种使用可变结构元的形态学分形维数估计算法。与四种传统的分形维数估计算法的对比实验显示，这种新算法不仅可以得到更加准确的分形纹理特征，而且算法的时间复杂度也更小。3、研究了基于多重分形模型的纹理特征。率先提出了基于数学形态学的多重分形估计算法，得到了一种全新的纹理描述符——局部形态学多重分形指数谱。与两种基于盒计数的多重分形维数相比，这种新特征在纹理图像分割实验中得到的分割精度更高，时间复杂度更小。此外，还将形态学多重分形估计与分形签名的概念相结合，提出了另一种纹理描述符——局部形态学多重分形签名。纹理图像分割实验表明，该特征的纹理区分能力不仅优于分形签名和局部形态学多重分形指数谱，也明显优于基于马尔可夫随机场模型的特征。4、研究了基于模糊聚类的图像分割技术。指出了图像的每一个纹理特征都可以被视为一个空间模式，提出了一种针对空间模式的模糊聚类算法实现了纹理图像分割。与经典的模糊聚类、空间模糊聚类和基于马尔可夫随机场模型的分割算法相比，新算法可以有效的提高纹理图像分割的精度。此外，还以该算法为核心，提出了一种基于图像四叉树的多级图像分割算法。对比实验显示，多级分割算法以牺牲少许分割精度为代价，将时间复杂度降低了一个数量级，从而使该算法可以被应用到数据量庞大且有一定实时性要求的场合。5、提出了耦合马尔可夫随机场模型来建模特征提取与图像分割之间的相互依赖关系，基于该模型实现了一种自适应的纹理图像分割算法。与经典的基于马尔可夫随机场的分割算法相比，新算法可以更好的定位纹理区域的边缘，从而显著的提高了纹理图像分割的精度。更多还原

【Abstract】 The segmentation of textured images aims to partition an image into severaldisjointed regions that are homogeneous with regards to some texture measures, sothat subsequent higher level computer vision processing can be performed. It has longbeen one of the most important branches of digital image processing and has drawnconsiderable attention of researchers from around the world. During the past threedecades, hundreds of segmentation algorithms have been proposed in the literature.However, due to the diversity of images, the complexity of natural textures and thelack of understanding of the human vision system (HVS), those algorithms usuallysuffer from less accuracy and narrow image specific orientation. Therefore, texturesegmentation is, up to now, still an open topic with great challenge in imageprocessing field.This dissertation is devoted to the semi-supervised segmentation of textured graylevel images, where the number of texture patterns is known but the information abouttheir properties is not. After comprehensively reviewing the basic principles andexisted methods, the author chooses the feature-based approaches to solve thisproblem. Generally, feature-based texture segmentation algorithms can be viewed asconsisting of two successive processes: feature extraction and feature partition.Feature extraction tends to find an appropriate descriptor to characterize thehomogeneity of each texture in an image so that all pixels from the same texture canbe represented by vectors of similar value. Feature partition is a process of assigningeach feature a label to designate the region or class to which it belongs, and thussegmentation result can be obtained through the relationship between features andpixels. In this dissertation, the author investigates those two processes, respectively,and achieves highly effective, increasingly innovative and cutting-edge approaches oftexture segmentation, which can be summarized as follows.1. An overview of segmentation of textured images, including the fundamentaldefinitions, the research background and the significance of this topic is presentedand the mainstream approaches and the state of art of algorithms in this field arereviewed in this dissertation.2. Texture feature extraction is investigated by using the fractal model in this dissertation. Various fractal dimensions have been widely used as texturedescriptors. However, the popular box-counting based fractal dimension iscommonly criticized for its less accuracy,, which is mainly caused by the regularpartition and counting scheme. Through analyzing the disadvantages of thetraditional morphological method, the author proposes a modified morphologicalfractal estimation approach, which uses a series of structure elements withdifferent scales to take the place of the unit structure elements used by traditionalmethod so that the estimation accuracy has been further improved. Throughdelicately selecting the shape of structure elements and constructing an iterativedilation scheme, the proposed approach substantially reduces the computationaltime-cost. When applied to texture segmentation, the novel morphological fractaldimension demonstrates an improved ability to differentiate various textures.3. Texture features based on the multifractal model is studied in this dissertation.Due to the limited bit depth and spatial resolution, most digital images are merelysemi-fractals and have anisotropic and inhomogeneous scaling properties.Therefore, fractal dimension alone is intrinsically not sufficient to representtexture patterns. To characterize the fractal reality of textured images, the authorgeneralizes the morphological fractal estimation algorithm to multifractalestimation, and thus proposes a novel texture descriptor called the localmorphological multifractal exponents (LMME). Furthermore, motivated by theidea of fractal signature, the author extends the LMME feature to themorphological multifractal signatures (MMFS). Those two multifractal texturefeatures has been compared with other commonly uses features in segmentation oftexture mosaics. The experimental results demonstrate that the novel features candifferentiate textured images more effectively and provide more robustsegmentations.4. Feature partition based on fuzzy clustering is explored in this dissertation. Featurepartition in feature-based texture segmentation is different from traditional patternclassification problems in that texture features imply not only the position infeature space but the position on image surface. Therefore, a texture feature isindeed a spatial pattern so that a textured image can be modeled as a set of spatialpatterns. The author proposes an approach to perceptual segmentation of imagesthrough the means of fuzzy clustering of spatial patterns, where the distancebetween a spatial pattern and each cluster is defined as a combination of the Euclidean distance in the feature space and the spatial dissimilarity, which reflectshow much of the pattern’s neighbourhood is occupied by other clusters. Theresults of comparative experiments demonstrate that the proposed approach cansubstantially improve the segmentation accuracy. Moreover, the author alsogeneralizes this approach to a multi-level feature partition algorithm, whichsignificantly decreases the computational complexity of texture segmentation.5. In feature-based texture segmentation, feature estimation and feature partition arenot two independent processes. Regardless of this fact, traditional methods usuallysuffer from the less accuracy, which is intrinsically caused by the oversimplifiedassumption that each textured sub-image used to estimate a feature ishomogeneous. To solve this problem, the author proposes a coupled Markovrandom field (CMRF) model, which has two coupled components: one models theobserved image to estimate features, and the other models the labeling to achievefeature partition. When calculating the feature of each pixel, the homogeneity ofthe sub-image is ensured by using only the pixels currently labeled as the samepattern. With the acquired features, and the labeling is obtained through solving aMAP (maximum a posteriori) problem. In this adaptive segmentation approach,the features and the labeling are mutually dependent on each other, and thereforeare alternately optimized by a simulated annealing scheme. With the gradualimprovement of features’ accuracy, the labeling is able to locate the exactboundary of each texture pattern. The proposed algorithm is compared with asimple MRF model based method in segmentation of both Brodatz texturemosaics and real scene images. The satisfying experimental results demonstratethat the proposed approach can differentiate textured images more accurately.更多还原