Modified Maximum Likelihood Estimation of the Spatial Resolution for the Elliptical Gamma Camera SPECT Imaging Using Binary Inhomogeneous Markov Random Fields Models

In this work a complete approach for estimation of the spatial resolution for the gamma camera imaging based on the [1] is analyzed considering where the body distance is detected (close or far way). The organ of interest most of the times is not well defined, so in that case it is appropriate to use elliptical camera detection instead of circular. The image reconstruction is presented which allows spatially varying amounts of local smoothing. An inhomogeneous Markov random field (M.r.f.) model is described which allows spatially varying degrees of smoothing in the reconstructions and a re-parameterization is proposed which implicitly introduces a local correlation structure in the smoothing parameters using a modified maximum likelihood estimation (MLE) denoted as one step late (OSL) introduced by [2].

Fusing PLSA model and Markov random fields for automatic image annotation

A novel image auto-annotation method is presented based on probabilistic latent semantic analysis(PLSA) model and multiple Markov random fields(MRF).A PLSA model with asymmetric modalities is first constructed to estimate the joint probability between images and semantic concepts,then a subgraph is extracted served as the corresponding structure of Markov random fields and inference over it is performed by the iterative conditional modes so as to capture the final annotation for the image.The novelty of our method mainly lies in two aspects:exploiting PLSA to estimate the joint probability between images and semantic concepts as well as multiple MRF to further explore the semantic context among keywords for accurate image annotation.To demonstrate the effectiveness of this approach,an experiment on the Corel5 k dataset is conducted and its results are compared favorably with the current state-of-the-art approaches.

Optimization by Estimation of Distribution with DEUM Framework Based on Markov Random Fields

This paper presents a Markov random field （MRP） approach to estimating and sampling the probability distribution in populations of solutions. The approach is used to define a class of algorithms under the general heading distribution estimation using Markov random fields （DEUM）. DEUM is a subclass of estimation of distribution algorithms （EDAs） where interaction between solution variables is represented as an undirected graph and the joint probability of a solution is factorized as a Gibbs distribution derived from the structure of the graph. The focus of this paper will be on describing the three main characteristics of DEUM framework, which distinguishes it from the traditional EDA. They are： 1） use of MRF models, 2） fitness modeling approach to estimating the parameter of the model and 3） Monte Carlo approach to sampling from the model.

Reservoir lithology stochastic simulation based on Markov random fields

Markov random fields(MRF) have potential for predicting and simulating petroleum reservoir facies more accurately from sample data such as logging, core data and seismic data because they can incorporate interclass relationships. While, many relative studies were based on Markov chain, not MRF, and using Markov chain model for 3D reservoir stochastic simulation has always been the difficulty in reservoir stochastic simulation. MRF was proposed to simulate type variables(for example lithofacies) in this work. Firstly, a Gibbs distribution was proposed to characterize reservoir heterogeneity for building 3-D(three-dimensional) MRF. Secondly, maximum likelihood approaches of model parameters on well data and training image were considered. Compared with the simulation results of MC(Markov chain), the MRF can better reflect the spatial distribution characteristics of sand body.

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain

To solve the problem that the magnetic resonance(MR)image has weak boundaries,large amount of information,and low signal-to-noise ratio,we propose an image segmentation method based on the multi-resolution Markov random field(MRMRF)model.The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales.The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm,and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation.The results are then segmented by the improved MRMRF model.In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model,it is proposed to introduce variable weight parameters in the segmentation process of each scale.Furthermore,the final segmentation results are optimized.We name this algorithm the variable-weight multi-resolution Markov random field(VWMRMRF).The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness,and can accurately and stably achieve low signal-to-noise ratio,weak boundary MR image segmentation.

CLOUD IMAGE DETECTION BASED ON MARKOV RANDOM FIELD

In order to overcome the disadvantages of low accuracy rate, high complexity and poor robustness to image noise in many traditional algorithms of cloud image detection, this paper proposed a novel algorithm on the basis of Markov Random Field (MRF) modeling. This paper first defined algorithm model and derived the core factors affecting the performance of the algorithm, and then, the solving of this algorithm was obtained by the use of Belief Propagation (BP) algorithm and Iterated Conditional Modes (ICM) algorithm. Finally, experiments indicate that this algorithm for the cloud image detection has higher average accuracy rate which is about 98.76% and the average result can also reach 96.92% for different type of image noise.

IMAGE SEGMENTATION BASED ON MARKOV RANDOM FIELD AND WATERSHED TECHNIQUES

This paper presented a method that incorporates Markov Random Field(MRF), watershed segmentation and merging techniques for performing image segmentation and edge detection tasks. MRF is used to obtain an initial estimate of x regions in the image under process where in MRF model, gray level x , at pixel location i , in an image X , depends on the gray levels of neighboring pixels. The process needs an initial segmented result. An initial segmentation is got based on K means clustering technique and the minimum distance, then the region process in modeled by MRF to obtain an image contains different intensity regions. Starting from this we calculate the gradient values of that image and then employ a watershed technique. When using MRF method it obtains an image that has different intensity regions and has all the edge and region information, then it improves the segmentation result by superimpose closed and an accurate boundary of each region using watershed algorithm. After all pixels of the segmented regions have been processed, a map of primitive region with edges is generated. Finally, a merge process based on averaged mean values is employed. The final segmentation and edge detection result is one closed boundary per actual region in the image.

Segmentation of MS lesions using entropy-based EM algorithm and Markov random fields

This paper presents an approach for fully automatic segmentation of MS lesions in fluid attenuated inversion recovery (FLAIR) Magnetic Resonance (MR) images. The proposed method estimates a gaussian mixture model with three kernels as cerebrospinal fluid (CSF), normal tissue and Multiple Sclerosis lesions. To estimate this model, an automatic Entropy based EM algorithm is used to find the best estimated Model. Then, Markov random field (MRF) model and EM algorithm are utilized to obtain and upgrade the class conditional probability density function and the apriori probability of each class. After estimation of Model parameters and apriori probability, brain tissues are classified using bayesian classification. To evaluate the result of the proposed method, similarity criteria of different slices related to 20 MS patients are calculated and compared with other methods which include manual segmentation. Also, volume of segmented lesions are computed and compared with gold standard using correlation coefficient. The proposed method has better performance in comparison with previous works which are reported here.

基于Markov随机场模型的数字X光图像自适应增强算法

为明确X光图像纹理粗细和组织分布状况,强化呈现身体结构信息,降低模糊图像对医生诊断病情结果的错误判断,提出一种基于Markov随机场模型的数字X光图像自适应增强算法.该算法首先统计X光图像全部范围内相同亮度像素,利用直方图均衡化法将原始图像变换成灰度级分布影像,消除光线干扰;然后分析组织属性,通过灰度共生矩阵提取X光图像的纹理特征,获取图像纹理粗细和布局结构的灰度信息;最后通过映射函数和对数函数计算平均亮度,用Markov随机场模型调整图像明暗度,补充纹理细小部位亮度,再用随机场函数划分光滑图像,采取二次重构,以保证图像锐化增强效果平衡.仿真实验结果表明,该算法能提升图像的内部信息清晰度.

一种基于TV模型结合MRF的图像修复算法

在图像修复的算法模型中,全变分TV(total variation)模型对于结构性强的图像拥有较好的修复效果,但对于图像纹理和边缘细节部分修复效果较不理想。而马尔可夫随机场(MRF)下FOE模型采用的邻域相关的系统方法在图像细节纹理修复方面有着出色的表现。故提出一种将两种模型结合的TV-FOE模型用于图像的修复,通过引进混合比例参数,使得改进的模型既能在保证对图像结构层次的修复效果,又能对图像纹理方面拥有很好的复原效果。将其与分别采用TV模型和FOE模型以及其他修复效果较好的算法进行对比,采用客观量化指标峰值信噪比(PSNR)、均方差(MSE)和像素差别图像进行分析、比较,量化结果证明所提出的TV-FOE模型对于破损图像拥有精度更高的修复效果。

城市实景模型结构化线面特征重构方法

为了改善城市实景模型边缘模糊,提出了一种曲率引导的结构化线面特征重构方法。根据曲率特征将网格分割为平面、可展凹、可展凸以及不可展曲面4类,在平面分割结果内提取平面,在可展凹和可展凸分割结果内提取直线,对过度弯曲的不可展区域进行保留,最终形成包含几何特征的复合网格模型。结果表明,结合曲率信息预先设置几何特征的潜在范围,使得结构化线面特征更可靠,同时保证城市实景中复杂的树结构不被错误地提取为平面。

基于生成MRF和局部统计特性的红外弱小目标检测算法

红外复杂背景中的弱小目标检测问题可看作是马尔可夫随机场理论框架下红外图像中背景与目标的二元分类标记问题.基于马尔可夫随机场后验概率模型,提出利用先验的目标信杂比信息和图像局部统计特性构建观测图像后验概率模型的方法,并采用经典ICM(Iterated conditional mode)方法对图像最优标记结果进行估计.仿真试验结果表明,算法在保证目标标记结果准确率的同时,有效降低了背景的误标记概率;且由于采用局部统计特性进行建模,算法有效降低了模型参数与标记结果间的关联性,提高了最优标记估计的收敛速度.

基于Markov约束的泊松最大后验概率超分辨率图象复原法

提出了基于 Markov约束的泊松最大后验概率 ( Poisson-MAP)超分辨率图象复原方法 ( MPMAP) ,该方法把 Poisson-MAP法和 Markov随机场先验分布有机地结合在一起 .实验表明 ,该方法能有效地减少和去除复原图象中的噪音和振荡条纹 ,提高图象复原质量 。

基于MAR-MRF的SAR图像分割方法

该文提出了一种基于多尺度自回归模型和马尔科夫随机场的SAR图像分割算法。算法引入多尺度自回归模型,建立层与层之间以及相邻层的像素点之间的数学关系,并将此模型与马尔科夫分割算法结合,实现了更为合理的多尺度分割策略。通过相邻尺度的依赖关系及同一尺度空间的马尔可夫性,使用多尺度自回归模型的预测结果来引导精细尺度图像分割,不仅使得最细尺度下的分割迭代次数减少;而且去除了最细尺度下多余的误分类斑块;同时还能够分割出清晰、平滑的目标边界,实现了较满意的SAR图像分割。

基于小波域TS-MRF模型的监督图像分割方法

定义在单一空间分辨率上的树结构马尔可夫场(Tree-Structured Markov Random Field,TS-MRF)模型能够表达图像的分层结构信息,但难以描述图像的非平稳性.针对该问题,提出小波域的TS-MRF图像建模方法—WTS-MRF模型.按照图像分类层次树的结构形式,该模型将一系列的MRF嵌套定义在多分辨率的小波域中:每一个树节点对应于定义在不同分辨率上的一个MRF集合,并通过条件概率的形式将相邻分辨率上的MRF间的作用关系考虑进来;同时相同分辨率的父子节点对应的MRF通过区域约束嵌套定义.基于WTS-MRF模型,给出了一个监督图像分割的递归算法,通过给定的分类层次树表示先验信息,并通过训练数据给出叶子节点在各分辨率上的统计参数.它在尺度内和尺度间两个层次上进行递归:首先,在最低分辨率上执行尺度内递归,即采用ICM算法从树的根节点到叶子节点依次对MRF进行递归估计;然后执行尺度间递归,即在相邻的更高分辨率尺度上,通过直接投影的方式依次获取每一MRF的初始估计,并采用ICM算法递归优化;最后,原始分辨率的MRF估计完成,获取最终分割结果.两组实验从视觉效果和定量指标(整体分类正确率和Kappa系数)两个方面验证了算法的有效性.

基于MRF的自适应正则化红外背景杂波抑制算法

针对复杂背景下红外弱小目标检测难题,将背景杂波抑制归结为从原始红外弱小目标图像中重建目标数据的过程,据此提出了一种基于马尔可夫随机场模型(MRF)的自适应正则化滤波算法.该算法采用MRF,建立了红外弱小目标图像的先验概率模型,并根据图像的粗糙度设计了新的势函数.在此基础上,采用MRF对背景杂波抑制过程进行正则化处理,从而实现了对红外背景杂波的自适应各向异性抑制.理论分析与实验结果表明,该算法能够随图像局部纹理特征的变化自适应地调整滤波算子结构,从而可在复杂背景下自适应地抑制杂波、增强信号,有效地提高了图像的信噪比,且该算法结构简单,更易于硬件实时实现.

SAR海冰的三维区域MRF图像分割

SAR海冰图像分割对气候变化研究和航行安全保障具有重要意义。现有MRF分割算法仅能利用到单个极化SAR图像中的信息,易受相干斑噪声和地物信息不全面性的影响,不能准确有效地完成分割。为此,在充分利用单个极化SAR图像相邻区域之间的相似性的基础上,进一步融入多个极化SAR图像相同区域之间的一致性,由此提出了一种三维区域MRF(3DRMRF)的SAR海冰图像分割算法,能够实现SAR海冰图像的准确分割。通过RADARSAT-2和SIR-C获得的单视全极化SAR海冰图像的实验结果表明:和其他较先进的算法相比,所提出的算法优势明显,特别是具有更高的分割精度。

基于模糊C均值与Markov随机场的图像分割

针对传统模糊C-均值(FCM)图像分割算法没有考虑图像空间连续性的缺点,提出一种改进的空间约束FCM分割算法。该算法引入了Markov随机场理论中类别标记的伪似然度近似策略,将像素特征域相似性同空间域相邻性有机地结合起来,给出了新的像素样本聚类目标函数。实验证明,该算法能大大提高分割性能并改善分割的视觉效果。

基于扩散方程和MRF的SAR图像分割

该文提出了一种基于图像扩散方程和马尔科夫随机场(MRF)的合成孔径雷达(SAR)图像分割方法。在传统MRF算法的基础之中,引入对图像的扩散,用来平滑SAR图像中的噪声,保护图像中的边缘部分,并且加快收敛的速度。首先对输入的SAR图像进行扩散,通过MRF进行统计,得到图像中各点的后验概率,再对得到的后验概率进行扩散。与传统的MRF算法进行比较,该文的方法较好地去除了误分割斑块,减少算法的运行时间。

基于复小波域树结构化MRF模型的声纳图像分割

声纳图像受成像环境影响对比度低,特性信息较弱,且图像分辨率不高,用传统的分割方法效果较差,为此,构建了双树复小波域树结构化的MRF模型(TS-MRF),提出了基于此模型的声纳图像分割算法。双树复小波变换(DT-CWT)具有近似平移不变性和良好的方向选择性,其多分辨率分析能有效地提取声纳图像的弱特征信息,以便TS-MRF中节点参数的描述能准确地反映树结构的分布规律和图像统计特性;复小波域6个方向高频子带相互独立,尺度间父子节点标号具有一阶Markov性;尺度内构建TS-MRF模型,且每一节点标号依赖于父节点,采用Potts模型对节点标号势函数建模,相同标号的观测特征用高斯模型建模;最后,用多分辨率递归和每一分辨率分类层次树从顶层向底层的尺度内递归算法来求解最大后验概率,实现分类层次树标号,完成声纳图像分割。实验结果从视觉效果和定量分析两方面验证表明,本文算法能有效地抑制噪声,较好地提取声纳图像的弱特征信息,具有较高的分割精度和鲁棒性。