Image denoising based on hierarchical Markov random field

We propose a hierarchical Markov random field model-based method for image denoising in this paper. The method employs a Markov random field (MRF) model with three layers. The first layer represents the underlying texture regions. The second layer represents the noise free image. And the third layer is the observed noisy image. Iterated conditional modes (ICM) is used to find the maximum a posteriori (MAP) estimation of the noise free image and texture region field. The experimental results show that the new method can effectively suppress additive noise and restore image details.     

Pattern recognition using Markov random field models

In this paper, we propose Markov random field models for pattern recognition, which provide a flexible and natural framework for modelling the interactions between spatially related random variables in their neighbourhood systems. The proposed approach is superior to conventional approaches in many aspects. This paper introduces the concept of states into Markov random filed models, presents a theoretic analysis of the approach, discusses issues of designing neighbourhood system and cliques, and analyses properties of the models. We have applied our method to the recognition of unconstrained handwritten numerals. The experimental results show that the proposed approach can achieve high performance.     

Hidden Markov random field models for TCA image analysis

Tooth Cementum Annulation (TCA) is an age estimation method carried out on thin cross sections of the root of mammalian teeth. Age is computed by adding the tooth eruption age to the count of annual incremental lines which are called tooth rings and appear in the cementum band. The number of rings is computed from an intensity (gray scale) image of the cementum band, by estimating the average ring width and then dividing the area of the cementum band by this estimate. The ring width is estimated by modelling the image by a hidden Markov random field, where intensities are assumed to be pixelwise conditionally independent and normally distributed, given a Markov random field of hidden binary labels, representing the“true scene”. To incorporate image macro-features (the long-range dependence among intensities and the quasi-periodicity in the placement of tooth rings), the label random field is defined by an energy function that depends on a parametric Gabor filter, convolved with the true scene. The filter parameter represents the unknown of main interest, i.e. the average width of the rings. The model is estimated through an EM algorithm, relying on the mean field approximation of the hidden label distribution and allows to predict the locations of the rings in the image.     

基于模糊马尔可夫场的脑部MR图像分割算法

在传统马尔可夫场模型的基础上,建立了模糊马尔可夫场模型。通过对模型的分析得出图像像素对不同类的隶属度计算公式,提出了一种高效、无监督的图像分割算法,从而实现了对脑部MR图像的精确分割。通过对模拟脑部MR图像和临床脑部MR图像分割实验,表明新算法比传统的基于马尔可夫场的图像分割算法和模糊C-均值等图像分割算法有更精确的图像分割能力。     

Deep learning the semantics of change sequences for query expansion

The overexpansion problem negatively affects the quality of query expansion. To improve the quality of queries for searching code, this paper proposed a DBN-based algorithm for effective query expansion. The deep belief network (DBN) model is trained on the code sequences and their change sequences, which aims to capture the meaningful terms during the evolution of source code. In contrast to previous studies, the proposed model not only extracts relevant terms to expand a query but also excludes irrelevant terms from the query. It addresses two problems in query expansion, including the overexpansion of the original query and the negative influence of the changed terms in the target source code. Experiments on both artificial queries and real queries show that the proposed algorithm outperforms several query expansion algorithms for code search.     

基于马尔科夫随机场的炉膛火焰图像分割

提出一种基于马尔科夫随机场模型的火焰图像分割算法。将由系统装置获取的原始火焰图像从RGB空间变换到HSV颜色空间,以提取颜色特征。分别对原始图像建立Potts标记场模型和有限正态混合观测场模型(FGMM),结合颜色特征,运用贝叶斯估计和ICM算法,计算最大后验概率(MAP),并完成图像分割。实验证明,该算法可以有效地分割炉膛火焰图像,为之后的工作奠定了基础。     

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。     

基于马尔可夫随机场的纹理图像并行分割

基于消息传递接口（Message Passing Interface,MPI）和消息传递并行编程模型,提出了一种针对计算机集群（Cluster）的纹理图像并行分割算法。该算法使用马尔可夫随机场作为纹理特征,通过将图像分块,把特征提取的计算量均匀的分布到并行系统中的各个节点上,从而极大地减少了计算时间。在遥感图像上的实验发现,该算法在4机并行的环境下可以取得与单机串行程序一样精确的分割,而耗时仅为串行程序的31.95％。令人满意的实验结果表明该并行算法不但可以有效的应用于纹理图像分割,而且也为使用计算机集群实现高时间复杂度的图像处理提供了有益的启示。     

Markov random fields for catadioptric image processing

Images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. Thus, Markov random fields (MRF) whose usefulness is now obvious for projective image processing, cannot be used directly on catadioptric images because of the inadequacy of the neighborhood. In this paper, we propose to define a new neighborhood for MRF by using the equivalence theorem developed for central catadioptric sensors. We show the importance of this adaptation for segmentation, image restoration and motion detection.     

基于马尔可夫随机场模型的运动对象分割算法

提出了一种基于马尔可夫随机场(MRF)模型的运动分割算法,仅使用了压缩流中的运动矢量和块编码模式信息,可以在复杂场景下对运动对象有很好的分割效果.利用运动矢量量化的方法来对运动矢量进行预处理,对运动矢量进行马尔可夫建模,利用能量最小函数进行优化得到运动对象分割的效果.实验表明:与现有的方法相比,该方法可从复杂场景中更准确地对运动对象进行分割.     

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。     

Queueing system with a phase process of query arrival in session

A multi-server quenching system with sessional query arrival is considered. The query arrival in each session is described by the considerably Phase Type (PH) arrival flow. The session acceptance in the system is limited with the help of tokens. Sessions that come into the system at the moment when free tokens are lacking are rejected. An algorithm for finding the joint distribution of the number of sessions and the number of queries in the system is provided. Several problems of the system’s parameters optimization are considered.     

Markerless human motion capture by Markov random field and dynamic graph cuts with color constraints

Currently, many vision-based motion capture systems require passive markers attached to key locations on the human body. However, such systems are intrusive with limited application. The algorithm that we use for human motion capture in this paper is based on Markov random field (MRF) and dynamic graph cuts. It takes full account of the impact of 3D reconstruction error and integrates human motion capture and 3D reconstruction into MRF-MAP framework. For more accurate and robust performance, we extend our algorithm by incorporating color constraints into the pose estimation process. The advantages of incorporating color constraints are demonstrated by experimental results on several video sequences. Supported by the National Basic Research Program of China (Grant No. 2006CB303105)     

基于隐马尔可夫随机场的社区结构发现算法

针对社区结构发现问题,提出了一种基于隐马尔可夫随机场社区发现算法.该方法将网络中的顶点度数映射为顶点信息值,用马尔可夫随机场模型描述网络中上下文信息并构造系统能量函数,使用迭代条件模式算法对能量方程进行优化.该方法在Zachary空手道俱乐部网络、海豚关系网络以及美国大学足球联赛网络上进行验证,实验结果表明,该算法的准确率较高.     

马尔可夫随机场理论在目标跟踪中的应用

在目标跟踪问题中,被跟踪目标的尺度变化、旋转变化和遮挡都会造成跟踪精确度的降低或目标的丢失。针对这些问题,提出了一种基于马尔可夫随机场的目标跟踪方法,将运动目标跟踪问题看作是前景和背景的二值分类问题,建立前景背景分割的马尔可夫随机场模型,从而实现对前景背景的分类,以完成对运动目标的跟踪。试验证明,这种方法可以有效地克服前景目标的尺度变化和旋转变化以及遮挡给目标跟踪带来的困难。     

基于PSO的Markov随机场在医学图像分割中的应用

研究了应用粒子群优化算法（PSO）优化Markov随机场方法对磁共振图像进行分割的算法。建立了基于马尔可夫随机场的图像分割模型,针对马尔可夫随机场图像模型的局部相关特性和最大后验概率估计,将粒子群优化算法应用于该模型,快速获得图像分割目标的全局最优解。实验数据表明该方法的高效性。     

Scale selection for anisotropic diffusion filter by Markov random field model

The selection of stopping time (i.e., scale) significantly affects the performance of anisotropic diffusion filter for image denoising. This paper designs a Markov random field (MRF) scale selection model, which selects scales for image segments, then the denoised image is the composition of segments at their optimal scales in the scale space. Firstly, statistics-based scale selection criteria are proposed for image segments. Then we design a scale selection energy function in the MRF framework by considering the scale coherence between neighboring segments. A segment-based noise estimation algorithm is also developed to estimate the noise statistics efficiently. Experiments show that the performance of MRF scale selection model is much better than the previous global scale selection schemes. Combined with this scale selection model, the anisotropic diffusion filter is comparable to or even outperform the state-of-the-art denoising methods in performance.     

基于各向异性Markov随机场的遥感影像亚像元尺度建筑物提取

基于遥感影像的建筑物自动提取方法容易受混合像元影响,目标提取精度不高。亚像元定位可以提取亚像元尺度地物分布信息,减轻混合像元对目标提取结果造成的影响。传统亚像元定位模型采用各向同性邻域描述地物的空间相关性,并没有考虑地物特有的形状信息,难以满足建筑物提取的需要。在考虑建筑物光谱特征的基础上,建立了平行与垂直于目标建筑物主方向的各向异性邻域,并采用基于各向异性Markov随机场的亚像元定位模型进行了亚像元尺度的建筑物提取。基于QuickBird多光谱数据与AVIRIS高光谱数据的实验结果表明,该模型提取的建筑物不仅具有更高的空间分辨率,而且能够较好地保持建筑物边缘与角点的形状信息,是一种有效的亚像元尺度建筑物提取方法。