基于对偶树复小波和MRF模型的纹理图像分割

基于对偶树复小波（DT-CWT）和马尔可夫随机场（MRF）模型提出了一种监督纹理图像分割算法,算法包括两个步骤,首先对复小波变换系数进行较为精确的建模,提取其一阶统计信息作为纹理特征,综合多个尺度的信息,基于极大似然标准进行初始分割;其次,将初始分割结果用MRF模型表示,基于贝叶斯最大后验（MAP）融合初始分割结果,得到最终的分割结果。算法应用于合成纹理图像和实际图像得到了良好的结果,对比实验表明算法所采用的纹理特征的提取方法、小波变换方式、用MRF模型来建模标号等是算法简洁有效的基础。     

基于MRF模型的NSCT域SAR图像分割

针对复杂背景下的合成孔径雷达（SAR）图像的分割问题,提出一种基于非降采样Contourlet变换（NSCT）域马尔可夫（MRF）模型的算法。该算法综合利用了MRF模型在影像分割中的优势和图像的多分辨率描述的信息,采用高斯混合模型建模各个尺度的特征场,Potts模型建模各个尺度的标记场,大尺度的分割结果直接投影到小尺度上,作为分割的初始结果。实验部分与经典的阈值分割算法和马尔可夫分割算法进行比较、分析,结果表明该算法可准确地分割目标,同时保留目标的细节信息。     

基于灰度共生矩阵和多尺度MRF的纹理图像分割

针对纹理图像分割问题的研究,经典的多尺度MRF方法是对不同尺度的纹理特征仅通过多尺度序列下的MRF邻域系统进行描述。为了更加准确地描述纹理特征,将从空间分布特性与MRF邻域系统两个方面综合考虑,提出一种带有联合灰度信息的灰度共生矩阵与多尺度MRF相结合的方法。实验结果表明,该方法能够有效地提高分割准确度。     

一种基于纹理特征的超声心动图像分割算法

以马尔可夫随机过程和梯度小波变换为基础提出了梯度小波纹理模型.由梯度小波纹理模型的参数组成描述图像纹理的特征向量,梯度小波纹理模型利用了马尔可夫随机场图像模型的优点和基于该模型的成熟方法,并且引入了多尺度、多分辨率等特性.然后采用Kohonen自组织SOM(Self-Organizing Feature Map)神经网络对纹理特征向量进行无监督学习,最后对超声心动图像进行纹理分割,取得较满意的分割效果.     

基于MRF模型的遥感图像建筑物分割研究

为了快速有效地从遥感图像中提取目标建筑物,采用小波分析和Markov随机场(MRF)相结合的方法进行遥感图像建筑物目标分割。首先将小波分解得到图像的多尺度序列作为各个尺度特征场的观测值,采用高斯混合模型建模特征场,以MRF模型作为标记场先验概率分布模型,通过EM算法迭代使得参数估计和图像分割交替进行,最后采用模板匹配检测建筑物目标的位置。选择多幅遥感图片进行实验,结果表明,采用分解层数为3的Haar小波,类别数为2,MLL模型势函数β为1时,该方法能够完成复杂背景下的建筑物目标分割并能对规则目标进行检测。     

一种轮廓波域MRF样本修补技术

提出轮廓波域MRF样本修补方法,通过轮廓波变换把待修补图像进行多尺度、多方向修补。实验结果表明,与传统的离散小波修补方法相比,轮廓波域MRF样本修补技术能更好抑制马赛克现象,保持良好的纹理和结构特征。     

一种基于多尺度马尔可夫随机场的SAR图像分割算法

利用小波变换模大值边缘检测算法得到SAR图像不同尺度下的边缘信息,再利用MRF分割算法对SAR图像进行分割。实验结果表明,该方法改善了SAR图像分割的质量,有效地改善了MRF图像分割算法的方向灵敏性。     

结合边界的小波域马尔科夫模型的图像分割算法

针对基于马尔科夫随机场(MRF)的分割算法常存在边界块效应,且对整幅图像进行建模运行效率低等问题,提出了结合边界的小波域马尔科夫模型的图像分割算法,把影像的特征场建立在一系列小波域提取的边界上,并建立相应的边界标号场MRF模型,借助贝叶斯框架和SMAP准则实现分割。利用Matlab GUI实现了分割系统,通过医学图像检验,结果表明:相比于小波域分层随机场模型(WMSRF),该算法在有效区分不同区域的同时很好地保留了边界信息,提高了运行效率。     

复小波域混合概率图模型的超声医学图像分割

针对存在大量不规则斑点噪声、目标边缘弱化的超声医学图像分割中较难识别目标的问题,提出了一种复小波域中混合概率图模型的超声医学图像分割算法.采用具有近似平移不变性和良好方向选择性的双树复小波变换(Dual treecomplex wavelet transform, DT-CWT)提取超声医学图像6个方向的高频特征信息;其次,为关联目标的弱特征信息并抑制统计独立的高频噪声,构建了复小波域混合概率图模型;尺度间"父–子"节点间标记采用贝叶斯网络进行建模,尺度内邻域间标记采用马尔科夫随机场(Markov randomfield, MRF)无向图建模,对复小波域中同尺度的特征系数采用高斯混合模型建模,尺度内同标记的观测特征采用高斯模型建模;最后,用迭代条件模式(Iterated conditional mode, ICM)实现MRF中误分割率最小的能量函数最优解,获取标记场,实现超声医学图像分割.实验结果从视觉效果和定量分析两方面验证表明,本文算法能有效地提取超声图像的弱目标信息,较好地定位目标区域,具有较高的分割精度和鲁棒性.     

融合多特征的纹理图像分割算法

针对传统的只用纹理的一种特征进行纹图像分割时的分割错误率较高的问题,提出了一种融合多特征的纹理图像分割算法.该方法综合考虑纹理的空间特征和频域特征,其中,空间特征提取在支持向量数据域描述的基础上进行;频域特征提取则利用改进的小波框架反映不同尺度间的特征;在此基础上,利用k均值算法对融合后的纹理特征进行聚类从而完成纹理图像的分割.实验结果表明与传统的只利用纹理的一种特征进行分割相比,该方法的错误率明显降低,同时在边缘准确性和区域一致性上都得到了明显的改善     

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。     

Texture classification using Gabor wavelets based rotation invariant features

Texture based image analysis techniques have been widely employed in the interpretation of earth cover images obtained using remote sensing techniques, seismic trace images, medical images and in query by content in large image data bases. The development in multi-resolution analysis such as wavelet transform leads to the development of adequate tools to characterize different scales of textures effectively. But, the wavelet transform lacks in its ability to decompose input image into multiple orientations and this limits their application to rotation invariant image analysis. This paper presents a new approach for rotation invariant texture classification using Gabor wavelets. Gabor wavelets are the mathematical model of visual cortical cells of mammalian brain and using this, an image can be decomposed into multiple scales and multiple orientations. The Gabor function has been recognized as a very useful tool in texture analysis, due to its optimal localization properties in both spatial and frequency domain and found widespread use in computer vision. Texture features are found by calculating the mean and variance of the Gabor filtered image. Rotation normalization is achieved by the circular shift of the feature elements, so that all images have the same dominant direction. The texture similarity measurement of the query image and the target image in the database is computed by minimum distance criterion.     

基于非下采样Brushlet和马尔可夫随机场的图像分割

针对传统小波域马尔可夫随机场图像分割算法的纹理图像分割能力的不足,提出一种将非下采样Brushlet变换和马尔可夫随机场相结合的纹理图像分割方法。用非下采样Brushlet变换作为图像分割的特征场,有效地提取纹理图像中的高维奇异信息;利用高斯马尔可夫模型提取特征场的参数,考察图像中的光谱信息以及像素点的空间相关性对分割结果的影响。实验表明,本文算法可以有效地实现纹理图像分割,在检测纹理方向信息和区域一致性上较传统算法有较大的提高。     

一种基于纹理特征融合的SAR图像分割方法

在由若干灰度共生矩阵纹理统计量进行特征融合后所生成的图像上,定义多分辨双Markov-GAR模型,采用多分辨MPM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。该方法既利用了像素的灰度信息,也利用了像素的空间位置信息,削弱了斑点噪声对分割的影响。实验表明对于一些高分辨SAR图像,该方法与单纯基于灰度图像上的多分辨双Markov-GAR模型纹理分割相比,分割精度得以提高。     

MRF based construction of statistical operator and its application

Based on the Markov random field (MRF) theory, a new nonlinear operator isdefined according to the statistical information in the image, and the corresponding 2Dnonlinear wavelet transform is also provided. It is proved that many detail coefficientsbeing zero (or almost zero) in the smooth gray-level variation areas can be achievedunder the conditional probability density function in MRF model, which shows that thisoperator is suitable for the task of image compression, especially for lossless codingapplications. Experimental results using several test images indicate good performancesof the proposed method with the smaller entropy for the compound and smooth medicalimages with respect to the other nonlinear transform methods based on median andmorphological operator and some well-known linear lifting wavelet transform methods(5/3, 9/7, and S+P).     

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.     

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。