基于层次Gamma混合模型的高分辨率SAR影像分割方法

为了准确地对高分辨率合成孔径雷达(SAR)影像内像素强度统计分布建模并得到高精度的分割结果,提出基于层次Gamma混合模型(HGaMM)的高分辨率SAR影像分割方法.HGaMM由多个Gamma混合模型构成,用于对非对称、重尾和多峰等复杂的像素强度统计分布建模.为了减少影像噪声对分割的影响,采用马尔科夫随机场建模像素标号场,将像素邻域位置关系引入HGaMM.根据贝叶斯理论,利用后验分布构建影像分割模型.马尔科夫链蒙特卡罗算法用于模拟影像分割模型.在模拟和真实SAR影像上的分割实验表明,文中方法可得到较高精度的分割结果.     

基于图像片马尔科夫随机场的脑MR图像分割算法

传统的高斯混合模型（Gaussian mixture model,GMM）算法在图像分割中未考虑像素的空间信息,导致其对于噪声十分敏感.马尔科 夫随机场（Markov random field,MRF）模型通过像素类别标记的Gibbs分布先验概率引入了图像的空间信息,能较好地分割含有噪声的图 像,然而MRF模型的分割结果容易出现过平滑现象.为了解决上述缺陷,提出了一种新的基于图像片权重方法的马 尔科夫随机场图像分割模型,对邻域内的不同图像片根据相似度赋予不同的权重,使其在克服噪声影响的同时能 保持图像细节信息.同时,采用KL距离引入先验概率与后验概率关于熵的惩罚项,并对该惩罚项进行平滑,得到 最终的分割结果.实验结果表明,算法具有较强的自适应性,能够有效克服噪声对于分割结果的影响,并获得较高的分割精度.     

基于多节点拓扑重叠测度高阶MRF模型的图像分割

针对低阶马尔科夫随机场(Markov random field, MRF)模型难以有效表达自然图像中复杂的先验知识而造成误分割问题, 提出一种基于多节点拓扑重叠测度高阶MRF模型(Higher-order MRF model with multi-node topological overlap measure, MTOM-HMRF)的图像分割方法. 首先, 为描述图像局部区域内多像素蕴含的复杂空间拓扑结构信息, 利用多节点拓扑重叠测度建立图像局部区域的高阶先验模型; 其次, 利用较大的局部区域包含更多的标签节点信息能力, 基于Pairwise MRF模型建立基于局部区域的部分二阶Potts先验模型, 提高分割模型的抗噪能力; 再次, 为有效描述观察图像场与其标签场的似然特征分布, 研究利用局部区域内邻接像素的Hamming距离引入图像局部空间相关性, 建立局部空间一致性约束的高斯混合分布; 最后, 基于MRF框架建立用于图像分割的多节点拓扑重叠测度高阶MRF模型, 采用Gibbs采样算法对提出模型进行优化. 实验结果表明, 提出模型不仅能有效抵抗图像强噪声和复杂的纹理突变干扰, 鲁棒性更好, 而且具有更准确的图像分割结果.     

核空间隐马尔可夫随机场图像模糊聚类

针对模糊C均值算法未考虑图像邻域信息,导致其分割效果不好的不足,结合隐马尔可夫随机场和高斯核函数,提出核空间隐马尔可夫随机场模糊C均值聚类算法。引入隐马尔可夫随机场,在目标函数中引入像素的空间邻域信息,使得分割算法对噪声鲁棒性增强;引入核函数,将样本点非线性变换映射到高维特征空间,增强图像分割的抗干扰能力,保持图像的细节信息。对标准灰度图像添加噪声,用以验证算法的性能。视觉效果及分割图像的峰值信噪比均显示,改进算法具有更好的抗噪能力。     

Multiple resolution segmentation of textured images

A multiple resolution algorithm is presented for segmenting images into regions with differing statistical behavior. In addition, an algorithm is developed for determining the number of statistically distinct regions in an image and estimating the parameters of those regions. Both algorithms use a causal Gaussian autoregressive model to describe the mean, variance, and spatial correlation of the image textures. Together, the algorithms can be used to perform unsupervised texture segmentation. The multiple resolution segmentation algorithm first segments images at coarse resolution and then progresses to finer resolutions until individual pixels are classified. This method results in accurate segmentations and requires significantly less computation than some previously known methods. The field containing the classification of each pixel in the image is modeled as a Markov random field. Segmentation at each resolution is then performed by maximizing the a posteriori probability of this field subject to the resolution constraint. At each resolution, the a posteriori probability is maximized by a deterministic greedy algorithm which iteratively chooses the classification of individual pixels or pixel blocks. The unsupervised parameter estimation algorithm determines both the number of textures and their parameters by minimizing a global criterion based on the AIC information criterion. Clusters corresponding to the individual textures are formed by alternately estimating the cluster parameters and repartitioning the data into those clusters. Concurrently, the number of distinct textures is estimated by combining clusters until a minimum of the criterion is reached     

Spatially Constrained Student’s <Emphasis Type="Italic">t</Emphasis>-Distribution Based Mixture Model for Robust Image Segmentation

The finite Gaussian mixture model is one of the most popular frameworks to model classes for probabilistic model-based image segmentation. However, the tails of the Gaussian distribution are often shorter than that required to model an image class. Also, the estimates of the class parameters in this model are affected by the pixels that are atypical of the components of the fitted Gaussian mixture model. In this regard, the paper presents a novel way to model the image as a mixture of finite number of Student’s t-distributions for image segmentation problem. The Student’s t-distribution provides a longer tailed alternative to the Gaussian distribution and gives reduced weight to the outlier observations during the parameter estimation step in finite mixture model. Incorporating the merits of Student’s t-distribution into the hidden Markov random field framework, a novel image segmentation algorithm is proposed for robust and automatic image segmentation, and the performance is demonstrated on a set of HEp-2 cell and natural images. Integrating the bias field correction step within the proposed framework, a novel simultaneous segmentation and bias field correction algorithm has also been proposed for segmentation of magnetic resonance (MR) images. The efficacy of the proposed approach, along with a comparison with related algorithms, is demonstrated on a set of real and simulated brain MR images both qualitatively and quantitatively.     

Unsupervised segmentation of noisy and inhomogeneous images using global region statistics with non-convex regularization

Improving the segmentation of magnetic resonance (MR) images remains challenging because of the presence of noise and inhomogeneous intensity. In this paper, we present an unsupervised, multiphase segmentation model based on a Bayesian framework for both MR image segmentation and bias field correction in the presence of noise. In our model, global region statistics are utilized as segmentation criteria in order to classify regions with similar mean intensities but different variances. Additionally, we propose an edge indicator function based on a guided filter (instead of a Gaussian filter) that can preserve the underlying edges of the image obscured by noise. The proposed edge indicator function is integrated with non-convex regularization to overcome the influence of noise, resulting in more accurate segmentation. Furthermore, the proposed model utilizes a Markov random field to model the spatial correlation between neighboring pixels, which increases the robustness of the model under high-noise conditions. Experimental results demonstrate significant advantages in terms of both segmentation accuracy and bias field correction for inhomogeneous images in the presence of noise.     

基于小波域层次Markov模型的图像分割

针对两个状态的有限高斯混合模型逼近小波系数的不足和小波域隐马尔可夫树标号场相互独立的缺点,提出了一种基于小波域层次马尔可夫模型的图像分割算法,这种模型用有限通用混合模型逼近小波系数的分布,使有限高斯混合模型只是其一种特殊情况;在标号场的先验模型确定上,利用马尔可夫模型描述标号场的局部作用关系,给出标号场的具体表达式,克服了小波域马尔可夫树模型标号场相互独立的不足,然后利用贝叶斯准则,给出相应的分割因果算法。该模型不仅具有空域马尔可夫模型有效的递归算法的优点,同时具有小波域隐马尔可夫树模型中的马尔可夫参数变尺度行为。最后用真实的图像和合成图像同几种分割方法进行了对比实验,实验结果表明了本文算法的有效性和优异性。     

形态小波域多尺度马尔可夫模型在纹理图像分割中的应用

针对图像分割中小波域多尺度马尔可夫模型(MRMRF-W)无法有效描述图像非线性特征,提出了一种在形态小波域下的多尺度MRF模型（MRMRF-MW）,实现纹理图像分割。该模型结合了形态小波和MRF各自的优势,能够对图像进行非线性多尺度分解,并在各尺度上进行空间关系建模。通过对两个纹理图像库（Brodatz纹理库、Prague纹理库）中图像的分割实验,验证了该模型的有效性。     

基于混合高斯模型MRF场的CT图像分割*

提出了一种基于混合高斯模型的马尔可夫随机场CT图像分割方法.此方法根据工业CT图像的特点,建立混合高斯逼近的图像灰度统计模型;用混合高斯模型作为Markov随机场的先验模型,提出混合高斯Markov随机场分割模型.实验表明,该方法较单高斯模型有很大的改善,对工业CT图像分割效果好.     

空间约束层次加权Gamma混合模型的SAR图像分割

目的 合成孔径雷达（SAR）图像中像素强度统计分布呈现出复杂的特性,而传统混合模型难以建模非对称、重尾或多峰等特性的分布。为了准确建模SAR图像统计分布并得到高精度分割结果,本文提出一种利用空间约束层次加权Gamma混合模型（HWGaMM）的SAR图像分割算法。方法 采用Gamma分布的加权和定义混合组份;考虑到同质区域内像素强度的差异性和异质区域间像素强度的相似性,采用混合组份加权和定义HWGaMM结构。采用马尔可夫随机场（MRF）建模像素空间位置关系,利用中心像素及其邻域像素的后验概率定义混合权重以将像素邻域关系引入HWGaMM,构建空间约束HWGaMM,以降低SAR图像内固有斑点噪声的影响。提出算法结合M-H（Metropolis-Hastings）和期望最大化算法（EM）求解模型参数,以实现快速SAR图像分割。该求解方法避免了M-H算法效率低的缺陷,同时克服了EM算法难以求解Gamma分布中形状参数的问题。结果 采用3种传统混合模型分割算法作为对比算法进行分割实验。拟合直方图结果表明本文算法具有准确建模复杂统计分布的能力。在分割精度上,本文算法比基于高斯混合模型（GMM）、Gamma分布和Gamma混合模型（GaMM）分割算法分别提高33%,29%和9%。在分割时间上,本文算法虽然比GMM算法多64 s,但与基于Gamma分布和GaMM算法相比较分别快600 s和420 s。因此,本文算法比传统M-H算法的分割效率有很大的提高。结论 提出一种空间约束HWGaMM的SAR图像分割算法,实验结果表明提出的HWGaMM算法具有准确建模复杂统计分布的能力,且具有较高的精度和效率。     

基于改进马尔可夫随机场的钢轨缺陷分割

为提高钢轨缺陷分割对噪声的鲁棒性,提出一种基于改进马尔可夫随机场(MRF)的钢轨缺陷分割方法。利用背景差分法对灰度进行预处理,消除灰度分布不均的干扰。对模糊if-then规则的前提部分采用马尔可夫随机场来利用图像中的空间约束,结果部分指定像素距离图算法,通过使用马尔可夫随机场(MRF)在相邻像素图像之间并入局部空间信息,推导出新的自适应模糊集和MRF相结合的钢轨表面缺陷自动分割方法。建立标准的FCM、GMM和该方法的钢轨缺陷分割对比实验,验证了算法的有效性和优越性。     

Improved HMRF for Remote Sensing Image Segmentation

The traditional Markov random field algorithm used for image segmentation is often associated with some known problems,such as unsmooth edges of the segmented regions due toimage noise and abnormal pixels values,thus,subsequently inaccuracy segmentation results.On account of this phenomenon,an algorithm that follows the hidden Markov random field which is based on finite Gaussian mixture model is put forward.First,the initial segmentation results are obtained by replacing traditional K-means method with the Expectation Maximization (EM) algorithm,and they are smoothedby using the bilateral filter.Next,the finite Gaussian mixture model and the Potts modelare used to model the feature field and the mark field,and the EM algorithm is used for its parameter estimationto obtain the feature field energy and the mark field energy.Finally,the energy function is minimized by using the Iterative Condition Model (ICM) algorithm in order to achievean optimal segmentation result.Experimental results show that our approach achieved a more efficient result by comparingto the classical MRF method and the traditional HMRF method,and the probabilistic rand index and global consistency error indicators are better than that of existing     

Study on segmentation and interpretation of multi-look polarimetric SAR images

The segmentation and interpretation of multi-look polarimetric synthetic aperture radar (SAR) images is studied. We first introduce a multi-look polarimetric whitening filter (MPWF) to reduce the speckle in multi-look polarimetric SAR images. Then, by utilizing the wavelet multiresolution approach to extract the texture information in different scales and the Markov random field (MRF) model to characterize the spatial constraints between pixels in each scale level, a multiresolution segmentation algorithm (MSA) to segment the speckle-reduced SAR images is presented. The MSA first segments the image at the lowest resolution level and then proceeds to progressively higher resolutions until individual pixels are well classified. An unsupervised step to estimate both the optimal number of texture classes and their model parameters is also included in the MSA so that the segmentation can be implemented without supervision. Finally, in order to interpret the results of the unsupervised segmentation and to understand the whole polarimetric SAR image, we develop an image interpretation approach which jointly utilizes the scattering mechanism identification and target decomposition approaches. Experimental results with the real-world multi-look polarimetric SAR image demonstrate the effectiveness of the segmentation and interpretation approaches.     

高斯Copula的多光谱遥感影像分割

为了充分利用多光谱影像波段间的相关性,提出高斯Copula的多光谱遥感影像分割方法.首先,建立基于马尔可夫随机场的标号场模型,使用Potts模型刻画该标号场.然后,建立表征像素光谱测度的特征场,利用高斯Copula建立像素光谱测度的多变量统计模型以刻画该特征场.结合标号场、特征场模型及各模型参数的先验概率,利用贝叶斯定理建立多光谱影像分割的后验概率模型.最后,设计适用于模拟后验概率模型的M-H算法,在最大后验概率策略下获取最优分割结果.对模拟和真实多光谱影像分割结果表明,文中方法描述波段间相关性的能力较强,准确性较高.     

基于证据马尔可夫随机场模型的图像分割

图像分割是计算机视觉中的经典问题,在许多领域都有重要应用.由于图像信息存在不确定性,难以获得精确的分割结果,为应对图像分割中的不确定性问题,将证据理论这一不确定性建模与推理工具与马尔可夫随机场相结合,提出证据马尔可夫随机场(EMRF)模型,并基于此提出新的图像分割算法.EMRF利用证据标号场描述像素标号的含混性,以证据距离描述相邻像素间的标号关系,利用条件迭代模型(ICM)算法进行优化.实验结果表明,EMRF相较于传统马尔可夫随机场、模糊马尔可夫随机场和传统的基于证据理论的方法,能获得更好的分割效果.     

一种基于一致性邻域超图模型的图象分割方法

提出了一种将超图理论与图象的空 -频域特征分析相结合的图象分割方法 .该方法是基于图象的多分辨率小波分析及高斯 -马尔可夫随机场理论 ,在抽取一组反映图象局部空间结构信息的特征矢量基础上 ,根据同一区域的象素具有相似的特征矢量的原则 ,将图象转换为一个关于特征相似性测度的邻域超图 ,再利用覆盖 -选择算法对该超图进行分割的方法 .实验证明 ,该方法具有较好的稳定性和适应性 ,尤其对于一些信噪比较低的图象 ,也具有良好的分割效果 .     

基于灰度共生矩阵纹理特征的SAR图像分割

同时考虑SAR图像局部灰度均值和方差及像素空间分布特征等统计量,在以灰度共生矩阵产生的纹理统计量为特征所生成的图像上,建立多分辨双Markov-GAR模型,采用多分辨MPM的参数估计方法及相应的无监督分割算法,对SAR图像进行纹理分割。该方法用于一些高分辨SAR图像,其分割精度及分割边缘的平滑度均优于基于灰度图像上的多分辨双Markov-GAR模型纹理分割。     

折棍变分贝叶斯图像分割算法

为提高图像分割的抗噪鲁棒性并解决分割数目的自适应确定问题,通过在聚类标签先验概率的折棍构造过程中建立Markov随机场,将空间相关性约束引入Dirichlet过程混合模型的概率建模,使聚类的空间平滑性得以增强,并采用变分推断方法获得聚类标签的收敛解析解,提出一种基于折棍变分贝叶斯推断的图像分割算法,实现了对像素聚类标签和分割数目的同步自适应学习,避免了传统方法中因引入空间相关性约束而出现的计算复杂问题.基于Berkeley BSD500图像测试数据集的数值实验结果表明,该算法具有比现有的混合模型聚类图像分割算法更高的PRI值,且在低于0.1的噪声方差条件下表现出了更优的抗噪鲁棒性.     

Turbo segmentation of textured images

We consider the problem of semi-supervised segmentation of textured images. Existing model-based approaches model the intensity field of textured images as a Gauss-Markov random field to take into account the local spatial dependencies between the pixels. Classical Bayesian segmentation consists of also modeling the label field as a Markov random field to ensure that neighboring pixels correspond to the same texture class with high probability. Well-known relaxation techniques are available which find the optimal label field with respect to the maximum a posteriori or the maximum posterior mode criterion. But, these techniques are usually computationally intensive because they require a large number of iterations to converge. In this paper, we propose a new Bayesian framework by modeling two-dimensional textured images as the concatenation of two one-dimensional hidden Markov autoregressive models for the lines and the columns, respectively. A segmentation algorithm, which is similar to turbo decoding in the context of error-correcting codes, is obtained based on a factor graph approach. The proposed method estimates the unknown parameters using the Expectation-Maximization algorithm.