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.     

MRF model and FRAME model-based unsupervised image segmentation

This paper presents a method for unsupervised segmentation of images consisting of multiple textures. The images under study are modeled by a proposed hierarchical random field model, which has two layers. The first layer is modeled as a Markov Random Field (MRF) representing an unobservable region image and the second layer uses "Filters, Random and Maximum Entropy (Abb. FRAME)" model to represent multiple textures which cover each region. Compared with the traditional Hierarchical Markov Random Field (HMRF), the FRAME can use a bigger neighborhood system and model more complex patterns. The segmentation problem is formulated as Maximum a Posteriori (MAP) estimation according to the Bayesian rule. The iterated conditional modes (ICM) algorithm is carried out to find the solution of the MAP estimation. An algorithm based on the local entropy rate is proposed to simplify the estimation of the parameters of MRF. The parameters of FRAME are estimated by the ExpectationMaximum (EM) algorithm. Finally, an exp     

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。     

Simple parallel hierarchical and relaxation algorithms for segmenting noncausal markovian random fields

The modeling and segmentation of images by MRF's (Markov random fields) is treated. These are two-dimensional noncausal Markovian stochastic processes. Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of C MRF's. The algorithms are designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology. A doubly stochastic representation is used in image modeling. Here, a Gaussian MRF is used to model textures in visible light and infrared images, and an autobinary (or autoternary, etc.) MRF to model a priori information about the local geometry of textured image regions. For image segmentation, the true texture class regions are treated either as a priori completely unknown or as a realization of a binary (or ternary, etc.) MRF. In the former case, image segmentation is realized as true maximum likelihood estimation. In the latter case, it is realized as true maximum a posteriori likelihood segmentation. In addition to providing a mathematically correct means for introducing geometric structure, the autobinary (or ternary, etc.) MRF can be used in a generative mode to generate image geometries and artificial images, and such simulations constitute a very powerful tool for studying the effects of these models and the appropriate choice of model parameters. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region.     

Unsupervised image segmentation using a simple MRF model with a new implementation scheme

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture) and achieves an improvement over the traditional method.     

Multispectral textured image segmentation using a multi-resolution fuzzy Markov random field model on variable scales in the wavelet domain

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas; image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. Segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms.     

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。     

A genetic algorithm for MRF-based segmentation of multi-spectral textured images

A segmentation approach based on a Markov random field (MRF) model is an iterative algorithm; it needs many iteration steps to approximate a near optimal solution or gets a non-suitable solution with a few iteration steps. In this paper, we use a genetic algorithm (GA) to improve an unsupervised MRF-based segmentation approach for multi-spectral textured images. The proposed hybrid approach has the advantage that combines the fast convergence of the MRF-based iterative algorithm and the powerful global exploration of the GA. In experiments, synthesized color textured images and multi-spectral remote-sensing images were processed by the proposed approach to evaluate the segmentation performance. The experimental results reveal that the proposed approach really improves the MRF-based segmentation for the multi-spectral textured images.     

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.     

基于多尺度MRF的膝关节MRI图像快速分割

膝关节MRI图像中骨骼的精确分割是进一步分割与定量分析膝部软组织的前提。目前膝关节骨骼分割的方法比较耗时或需要一定的人机交互。为解决这一问题,将多尺度MRF方法引入到膝关节MRI分割中,以实现快速无监督的分割。首先建立高斯混合的灰度统计模型,运用MDL准则自动确定类别的数目。建立多尺度MRF的先验模型时,利用尺度间的因果性给出非迭代的计算方法,由细尺度往粗尺度传递统计信息,再由粗尺度往细尺度计算每个像素的最大后验概率,从而实现快速准确的分割。实验结果表明,与单尺度MRF相比,多尺度MRF分割膝关节MRI所需时间大大减少,且精度与专家手动分割标准相当。算法通过建立多尺度马尔可夫随机场模型,完成了低信噪比膝关节MRI图像快速准确分割,可作为进一步自动分割软骨与半月板等软组织的基础。     

基于马尔可夫随机场的SAR图象目标分割

运动、静止目标获取与识别（MSTAR）计划表明，将合成孔径雷达（SAP）图象分割成目标、阴影和背景杂波区域对于从开放环境中进行目标识别是一种有效的手段。但是由于SAP图象所固有的斑点噪声的影响，传统的分割方法很难获得准确的分割。为此提出了一种基于MRF(Markov random field)模型的SAP图象分割算法。用MRF模型描述待分割图象的先验知识，利用最大似然（ML）估计从训练数据中获得图象各区域的先验概率分布，采用Bayes方法，在观测数据基础上，根据分割图象的后验分布所对应的MRF模型的条件概率，利用Metroplis采样器获得最大后验概率（MAP）准则下的图象分割。通过对MSTAR的样本目标图象应用该算法，结果表明它可以获得稳健和准确的分割效果。     

Spectral–spatial classification of hyperspectral images by algebraic multigrid based multiscale information fusion

In this work, we present a novel spectral-spatial classification framework of hyperspectral images (HSIs) by integrating the techniques of algebraic multigrid (AMG), hierarchical segmentation (HSEG) and Markov random field (MRF). The proposed framework manifests two main contributions. First, an effective HSI segmentation method is developed by combining the AMG-based marker selection approach and the conventional HSEG algorithm to construct a set of unsupervised segmentation maps in multiple scales. To improve the computational efficiency, the fast Fish Markov selector (FMS) algorithm is exploited for feature selection before image segmentation. Second, an improved MRF energy function is proposed for multiscale information fusion (MIF) by considering both spatial and inter-scale contextual information. Experiments were performed using two airborne HSIs to evaluate the performance of the proposed framework in comparison with several popular classification methods. The experimental results demonstrated that the proposed framework can provide superior performance in terms of both qualitative and quantitative analysis.     

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.     

Segmentation of fuzzy images: a novel and fast two-step pseudo MAP method

This paper presents a new two-step pseudo maximum a posteriori (MAP) segmentation method for the Markov random field (MRF)-modeled image because the exact MAP estimation is hard to implement due to intractable complexity. The expectation maximization (EM) and Markov Chain Monte Carlo (MCMC) methods are adopted to estimate the parameters for the MRF model due to their comparatively good performance. Although the image segmentation algorithms via graph cuts have become very popular nowadays, our proposed algorithm still performs significantly better in automatic identification and segmentation of fuzzy images than them, which is shown by the quantitative results on synthesized images. In practical applications, the proposed two-step pseudo MAP method is superior in segmenting the fuzzy laser images reflected from the weld pool surfaces during the P-GMAW welding process.     

基于层次MRF的MR图像分割

核磁共振图像(MRI)的定量分析在神经疾病的早期治疗中有很重要作用.提出了一种基于层次Markov随机场模型的MRI图像分割新方法.在高层次的标记图象中采用了混合模型,即区域的内部用各向同性均匀MRF来建模,边界用各向异性非均匀MRF来建模.所以方向性被引入到边界信息中,这样可以更准确的表达标记图象的特性;在低层次的像素图像中,不同区域中像素的灰度分布用不同的高斯纹理来描述.分割问题可以被转换成一种最大后验概率估计问题.采用基于直方图的DAEM算法来估计SNFM参数的全局最优值;并基于MRF先验参数的实际意义,提出一种近似的方法来简化这些参数的估计,实验显示该方法能获得更好的结果.     

结合MRF能量和模糊速度的乳腺癌图像分割方法

乳腺癌灶的精确分割是乳腺癌计算机辅助诊断的重要前提. 在动态对比增强核磁共振成像(Dynamic contrast-enhanced magnetic resonance imaging, DCE-MRI)的图像中, 乳腺癌灶具有对比度低、边界模糊及亮度不均匀等特点, 传统的活动轮廓模型方法很难取得准确的分割结果. 本文提出一种结合马尔科夫随机场(Markov random field, MRF)能量和模糊速度函数的活动轮廓模型的半自动分割方法来完成乳腺癌灶的分割, 相对于专业医生的手动分割, 本文方法具有速度快、可重复性高和分割结果相对客观等优点. 首先, 计算乳腺DCE-MRI图像的MRF能量, 以增强目标区域与周围背景的差异. 其次, 在能量图中计算每个像素点的后验概率, 建立基于后验概率驱动的活动轮廓模型区域项. 最后, 结合Gabor纹理特征、DCE-MRI时域特征和灰度特征构建模糊速度函数, 将其引入到活动轮廓模型中作为边缘检测项. 在乳腺癌灶边界处, 该速度函数趋向于零, 活动轮廓曲线停止演变, 完成对乳腺癌灶的分割. 实验结果表明, 所提出的方法有助于乳腺癌灶在DCE-MRI图像中的准确分割.     

Segmentation for remote-sensing imagery using the object-based Gaussian-Markov random field model with region coefficients

The Markov random field (MRF) model is a widely used method for remote-sensing image segmentation, especially the object-based MRF (OMRF) method has attracted great attention in recent years. However, the OMRF method usually fails to capture the correlation between regional features by just considering the mixed-Gaussian model. In order to solve this problem and improve the segmentation accuracy, this article proposes a new method, object-based Gaussian-Markov random field model with region coefficients (OGMRF-RC), for remote-sensing image segmentation. First, to describe the complicated interactions among regional features, the OGMRF-RC method employs the region size and edge information as region coefficients to build the object-based linear regression equation (OLRE) for each region. Second, the classic Gaussian-Markov model is extended to region level for modelling the errors in OLREs. Finally, the segmentation is achieved through a principled probabilistic inference designed for the OGMRF-RC method. Experimental results over synthetic texture images and remote-sensing images from different datasets show that the proposed OGMRF-RC method can achieve more accurate segmentation than other state-of-the-art MRF-based methods and the method using convolutional neural networks.     

结合MRF模型与粒子群优化算法的遥感影像纹理分割

影像纹理的马尔可夫随机场(MRF)模型是一种分析纹理较为经典的方法,已被广泛用于影像纹理的模拟和分割。由于传统的模拟退火算法在计算全局最优解时,处理效率较低,无法满足纹理分析与处理的性能要求。设计了一种判定纹理类别的适应度函数,提出了利用粒子群优化算法计算适应度函数的最优解,应用该算法对遥感影像数据进行了纹理分割实验。实验结果表明,该算法与模拟退火算法比较,具有寻优速度快的优点,是一种有效的图像分割优化方法。     

VBI-MRF model for image segmentation

In statistical image segmentation, the distribution of pixel values is usually assumed to be Gaussian and the optimal result is believed to be the one that has maximum a posteriori (MAP) probability. In spite of its prevalence and computational efficiency, the Gaussian assumption, however, is not always strictly followed, and hence may lead to less accurate results. Although the variational Bayes inference (VBI), in which statistical model parameters are also assumed to be random variables, has been widely used, it can hardly handle the spatial information embedded in pixels. In this paper, we incorporate spatial smoothness constraints on pixels labels interpreted by the Markov random field (MRF) model into the VBI process, and thus propose a novel statistical model called VBI-MRF for image segmentation. We evaluated our algorithm against the variational expectation-maximization (VEM) algorithm and the hidden Markov random field (HMRF) model and MAP-MRF model based algorithms on both noise-corrupted synthetic images and mosaics of natural texture. Our pilot results suggest that the proposed algorithm can segment images more accurately than other three methods and is capable of producing robust image segmentation.     

一种鲁棒的MRF-MAP图象分割框架研究

提出了一种新颖的基于马尔可夫随机场(MRF)空间上下文信息的图象分割方法。该方法利用马尔可夫随机场表示图象标记场,并在传统的邻域势函数基础上,引入观测场中邻域像素间强度关系,由此描述像素被分入同一类的可能性。通过贝叶斯(Bayes)定理将分割问题转化为最大后验(MAP)估计的问题。运用迭代条件模型(ICM)求取最大后验估计的解。用人工合成图象及真实图象进行实验,同时与传统的期望最大化(EM)方法以及传统的马尔可夫随机场方法相比较,由实验结果及信噪比(SNR)-误分率(MCR)曲线可以看出,该文的方法对噪声图象分割更为有效。