Markov random field models for unsupervised segmentation oftextured color images

We present an unsupervised segmentation algorithm which uses Markov random field models for color textures. These models characterize a texture in terms of spatial interaction within each color plane and interaction between different color planes. The models are used by a segmentation algorithm based on agglomerative hierarchical clustering. At the heart of agglomerative clustering is a stepwise optimal merging process that at each iteration maximizes a global performance functional based on the conditional pseudolikelihood of the image. A test for stopping the clustering is applied based on rapid changes in the pseudolikelihood. We provide experimental results that illustrate the advantages of using color texture models and that demonstrate the performance of the segmentation algorithm on color images of natural scenes. Most of the processing during segmentation is local making the algorithm amenable to high performance parallel implementation     

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.     

Markov random field texture models

We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models. The binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors and ``number of tries' equal to the number of gray levels, was taken to be the basic model for the analysis. A method of generating samples from the binomial model is given, followed by a theoretical and practical analysis of the method's convergence. Examples show how the parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Natural texture samples were digitized and their parameters were estimated under the Markov random field model. A hypothesis test was used for an objective assessment of goodness-of-fit under the Markov random field model. Overall, microtextures fit the model well. The estimated parameters of the natural textures were used as input to the generation procedure. The synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.     

基于马尔可夫随机场的加密二值图像有损压缩算法

尽管当前已有众多二值图像的压缩方法,但这些方法并不能直接应用于加密二值图像的压缩。在云计算、分布式处理等场景下,如何高效地对加密二值图像进行有损压缩仍然是一个挑战,而当前鲜有这方面的研究。针对此问题,提出了一种基于马尔可夫随机场(MRF)的加密二值图像有损压缩算法。该算法用MRF表征二值图像的空域统计特性,进而借助MRF及解压缩还原的像素推断加密二值图像压缩过程中被丢弃的像素。所提算法的发送方采用流密码对二值图像进行加密,云端先后利用分块均匀但块内随机的下抽样方式及低密度奇偶校验(LDPC)编码对加密二值图像进行压缩,接收方则通过构造包含解码、解密及MRF重构的联合因子图实现二值图像的有损重构。实验结果表明,所提算法获得了较好的压缩效率,在0.2~0.4 bpp压缩率时有损重构图像的比特误差率(BER)不超过5%;而与针对未加密原始二值图像的国际压缩标准JBIG2的压缩效率相比,所提算法的压缩效率与其相当。这些充分表明了所提算法的可行性与有效性。     

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.     

Unsupervised texture segmentation using Markov random field models

The problem of unsupervised segmentation of textured images is considered. The only explicit assumption made is that the intensity data can be modeled by a Gauss Markov random field (GMRF). The image is divided into a number of nonoverlapping regions and the GMRF parameters are computed from each of these regions. A simple clustering method is used to merge these regions. The parameters of the model estimated from the clustered segments are then used in two different schemes, one being all approximation to the maximum a posterior estimate of the labels and the other minimizing the percentage misclassification error. The proposed approach is contrasted with the algorithm of S. Lakshamanan and H. Derin (1989), which uses a simultaneous parameter estimation and segmentation scheme. The results of the adaptive segmentation algorithm of Lakshamanan and Derin are compared with a simple nearest-neighbor classification scheme to show that if enough information is available, simple techniques could be used as alternatives to computationally expensive schemes     

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 and facial landmarks for handling uncontrolled images of face sketch synthesis

Pattern Analysis and Applications - Face sketch synthesis has drawn great attention in many computer vision applications such as law enforcement and digital entertainment. The majority of existing...     

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。     

Parameter estimation in Markov random field contextual models usinggeometric models of objects

We present a new scheme for the estimation of Markov random field line process parameters which uses geometric CAD models of the objects in the scene. The models are used to generate synthetic images of the objects from random view points. The edge maps computed from the synthesized images are used as training samples to estimate the line process parameters using a least squares method. We show that this parameter estimation method is useful for detecting edges in range as well as intensity edges. The main contributions of the paper are: 1) use of CAD models to obtain true edge labels which are otherwise not available; and 2) use of canonical Markov random field representation to reduce the number of parameters     

Texture classification using features derived from random field models

This paper presents a new feature extraction method for classifying a texture image into one of the l possible classes C_i, i=1,…,l. It is assumed that the given M × M image characterized by a set of intensity levels, {y(s₁,S₂), 0≤s_s,s₂≤M?1}, is a realization of an underlying random field model, known as the Simultaneous Autoregressive Model (SAR). This model is characterized by a set of parameters φ whose probability density function p_i(φ), depends on the class to which the image belongs. First it is shown that the maximum likelihood estimate (M.L.E.) $\text{φ}{}^1$, of φ is an appropriate feature vector for classification purposes. The optimum Bayes classifier which minimizes the average probability of classification error, is then designed using $\text{φ}{}^1$. Finally the efficiency of the feature vector is demonstrated through experimental results obtained with some natural texture data and a simpler quadratic mean classifier.     

The synthesis and analysis of color images

I describe a method for performing the synthesis and analysis of digital color images. The method is based on two principles. First, image data are represented with respect to the separate physical factors, surface reflectance and the spectral power distribution of the ambient light, that give rise to the perceived color of an object. Second, the encoding is made efficient by using a basis expansion for the surface spectral reflectance and spectral power distribution of the ambient light that takes advantage of the high degree of correlation across the visible wavelengths normally found in such functions. Within this framework, the same basic methods can be used to synthesize image data for color display monitors and printed materials, and to analyze image data into estimates of the spectral power distribution and surface spectral reflectances. The method can be applied to a variety of tasks. Examples of applications include the color balancing of color images and the identification of material surface spectral reflectance when the lighting cannot be completely controlled.     

Image estimation using doubly stochastic gaussian random field models

The two-dimensional (2-D) doubly stochastic Gaussian (DSG) model was introduced by one of the authors to provide a complete model for spatial filters which adapt to the local structure in an image signal. Here we present the optimal estimator and 2-D fixed-lag smoother for this DSG model extending earlier work of Ackerson and Fu. As the optimal estimator has an exponentially growing state space, we investigate suboptimal estimators using both a tree and a decision-directed method. Experimental results are presented.     

Change detection method for remote sensing images based on an improved Markov random field

The fixed weights between the center pixel and neighboring pixels are used in the traditional Markov random field for change detection, which will easily cause the overuse of spatial neighborhood information. Besides the traditional label field cannot accurately identify the spatial relations between neighborhood pixels. To solve these problems, this study proposes a change detection method based on an improved MRF. Linear weights are designed for dividing unchanged, uncertain and changed pixels of the difference image, and spatial attraction model is introduced to refine the spatial neighborhood relations, which aims to enhance the accuracy of spatial information in MRF. The experimental results indicate that the proposed method can effectively enhance the accuracy of change detection.     

基于图像块分类器和条件随机场的显微图像分割

针对花粉显微图像处理提出了一种自动分割方法,将有助于花粉识别系统的开发。使用归一化颜色分量训练图像块分类器,并且结合条件随机场和图割进行建模和优化,利用最大化后验概率(MAP)的方法实现花粉显微图像中花粉区域的分割。对于实验中的133幅图像,自动分割同人工分割的结果相比较,统计得到距离误差均值为7.3像素,准确率的平均值为87%。实验结果表明,使用图像块分类器和条件随机场模型可以用于花粉图像的分割。     

Similarity analysis in Bayesian random partition models

This work proposes a method to assess the influence of individual observations in the clustering generated by any process that involves random partitions. We call it Similarity Analysis. It basically consists of decomposing the estimated similarity matrix into an intrinsic and an extrinsic part, coupled with a new approach for representing and interpreting partitions. Individual influence is associated with the particular ordering induced by individual covariates, which in turn provides an interpretation of the underlying clustering mechanism. We present applications in the context of Species Sampling Mixture Models (SSMMs), including Bayesian density estimation and dependent linear regression models.     

Manifold models for signals and images

This article proposes a new class of models for natural signals and images. These models constrain the set of patches extracted from the data to analyze to be close to a low-dimensional manifold. This manifold structure is detailed for various ensembles suitable for natural signals, images and textures modeling. These manifolds provide a low-dimensional parameterization of the local geometry of these datasets. These manifold models can be used to regularize inverse problems in signal and image processing. The restored signal is represented as a smooth curve or surface traced on the manifold that matches the forward measurements. A manifold pursuit algorithm computes iteratively a solution of the manifold regularization problem. Numerical simulations on inpainting and compressive sensing inversion show that manifolds models bring an improvement for the recovery of data with geometrical features.     

Multispectral images fusion by a joint multidirectional and multiresolution representation

This paper proposes a new fusion method that permits an adequate selection of information extracted from source images to obtain fused images with good spatial and spectral quality simultaneously. This method is based on a joint multiresolution multidirectional representation of the source images using a single directional spatial frequency low pass filter bank of low computational complexity, defined in the Fourier domain. The source images correspond to those captured by the IKONOS satellite (panchromatic and multispectral). The results obtained indicate that the proposed method provides, in a simple manner, objective control over the trade‐off between high spatial and spectral quality of the fused images.     

Spatially adaptive smoothing parameter selection for Markov random field based sub-pixel mapping of remotely sensed images

Sub-pixel mapping is a process to provide the spatial distributions of land cover classes with finer spatial resolution than the size of a remotely sensed image pixel. Traditional Markov random field-based sub-pixel mapping (MRF_SPM) adopts a fixed smoothing parameter estimated based on the entire image to balance the spatial and spectral energies. However, the spectra of the remotely sensed pixels are always spatially variable. Adopting a fixed smoothing parameter disregards the local properties provided by each pixel spectrum, and may probably lead to insufficient smoothing in the homogeneous region and over-smoothing between class boundaries simultaneously. This article proposes a spatially adaptive parameter selection method for the MRF_SPM model to overcome the limitation of the fixed parameter. As pixel class proportions are indicators of the type and proportion of land cover classes within each coarse pixel, in the proposed method, fraction images providing pixel class proportions as local properties of each pixel spectrum are employed to constrain the smoothing parameter. Consequently, the smoothing parameter is spatially adaptive to each pixel spectrum of the remotely sensed image. Synthetic images and IKONOS multi-spectral images were employed. Results showed that compared with the hard classification method and the non-spatially adaptive MRF_SPM adopting a fixed smoothing parameter, the spatially adaptive MRF_SPM with the smoothing parameter constrained to each pixel spectrum yielded sub-pixel maps not only with higher accuracy but also with shapes and boundaries visually reconstructed more closely to the reference map.