基于多模方法的位移矢量场自适应估计

半平面ＣＧＭ（ｈａｌｆ ｐｌａｎｅ ｃｏｍｐｏｕｎｄ Ｇａｕｓｓ－Ｍａｒｋｏｖ）模型是以线过程（ｌｉｎｅ ｐｒｏｃｅｓｓ）标识不同的子模型,适合于描述平稳和非平稳的位移矢量场ＤＶＦ（ｄｉｓｐｌａｃｅｍｅｎｔ ｖｅｃｔｏｒ ｆｉｅｄｌ）;半平面ＭＲＦ（Ｍａｒｋｏｖ ｒａｎｄｏｍ ｆｉｅｌｄ）模型描述线过程的分布,以确定各个子模型的先验概率。由此,本文提出了一种基于多模方法的递归自适应ＤＶＦ估计算法。     

结合背景抑制技术的FLIR图像分割

针对前视红外（ＦＬＩＲ）图像的分割,提出采用去中值波滤器进行预处理抑制背景、增强目标,进而利用基于模型的ＦＬＩＲ图像分割（ＭＢＳ）算法完成图像分割,从相容性向量及初始概率计算两方面对ＭＢＳ算法进行了改进．对实际红外目标图像分割结果证实该方法与ＭＢＳ算法相比,在低对比度、高噪声情况下能得到更为精确的分割结果,同时能极大地降低了背景干扰     

结合背景抑制技术的ELIR图像分割

针对前视红外（ＦＬＩＲ）图像的分割,提出采用去中值波滤器进行预处理抑制背景、增强目标,而进利用基于模型的ＦＬＩＲ图像分割（ＭＢＳ）算法完成图像分割,从相容性向量及初始概率计算两方面对ＭＢＳ算法进行了改进,对红外目标图结果证实该方法与ＭＢＳ算法相比,在低对比度、高噪声情况下能得到更为精确的分割结果,同时能极大地降低了背景干扰。     

利用随机图象模型改善前视红外图象分割

本文提出一种可从前视红外（ＦＬＩＲ）图象中提取目标的改进式分割算法。认为所观察ＦＬＩＲ图象是由三种随机模型构成的：第一种模型是由噪声起支配作用，并假定是非相关高斯分布；第二种模型中两个区域（即目标和背景）的标志应遵从Ｇｉｂｂ无规则场（ＧＲＦ）分布；最后，采用群参数表示目标尺寸与背景尺寸之比。群参数易于产生相似尺寸的分割。确立随机模型后，提出确定区域标志的最大的后验（ＭＡＰ）估算。利用一种确定的逐次近似法实现ＭＡＰ准则的优选，以便迅速收敛到局部最大值。     

SAR图像基于Rayleigh分布假设的最小误差阈值化分割

针对合成孔径雷达（ＳＡＲ）图像的特点，本文提出基于灰度直方图的混合偏移Ｒａｙｌｅｉｇｈ分布假设下的最小误差阈值化分割算法，并与现有的基于Ｇａｕｓｓ和Ｐｏｉｓｓｏｎ分布假设下的最小误差分割算法以及经典的Ｏｔｓｕ算法作了比较。实验和Ｋｏｌｍｏｇｏｒｏｖ－Ｓｍｉｒｎｏｖ检验结果表明对ＳＡＲ图像而言，基于Ｒａｙｌｅｉｇｈ假设的算法可以取得更好的分割效果。     

FCM算法用于灰度图象分割的研究

模糊Ｃ均值（ＦＣＭ）算法用于灰度图象分割是一种非监督模糊聚类后再标定的过程，适合灰度图象中存在着模糊和不稳定性的特点，但是这种算法存在着一些不足，如类数目无法自动确定，运算的开销太大等，因而限掉了这种方法的应用，针对这些问题，本文利用直方图分析的方法，自动确定算法的聚类数目和各类的类峰值，并针对ＦＣＭ算法和灰度图象的特点，提出了一种适用于灰度图象分割的快速ＦＣＭ算法（ＱＦＣＭ）使得运算了开销降低，     

一种改进的模糊聚类算法

ＦＣＭ（Ｆｕｚｚｙ Ｃ－Ｍｅａｎｓ）算法是一种基于目标函数优化的模糊聚类方法，其收敛地于初始条件敏感。与ＨＣＭ（Ｈａｒｄ Ｃ－Ｍｅａｎｓ）算法相比，ＦＣＭ算法的模糊分割矩阵提供的信息更加丰富。本文采用冗余聚类中心初始化，根据模糊分割矩 列和以及实际的要求逐级减少类别数目。实验结果显示改进的算法得到的收敛中心稳定，并且中以融合有关数据分布的先验知识得到所期望的结果。     

基于FGM-MRF模型的图像分割

利用Ward聚类将图像进行初始分割,其结果作为基于空间邻域信息马尔可夫随机场(MRF)模型对图像再次分割的初值,图像分割的先验概率采用Ising模型,通过有限高斯混合模型(FGM)描述图像像素灰度的条件概率分布,利用期望-最大(EM)算法估计条件概率分布模型参数,用迭代条件模式(ICM)局部优化方法,获得最大后验概率(MAP)准则下的图像分割结果.通过与其他相关算法分割结果相比较,这种算法能够明显改善分割效果.     

基于广义径向基函数神经网络的非线性时间序列预测器

该文对传统的径向基函数（ＲＢＦ）神经网络的结构和学习算法进行了总结,并在此基础上提出了广义径向基函数模型概念,使这种网络具有更好的应用灵活性与可扩充性。文章基于Ｍａｃｋｅｙ－Ｇｌａｓｓ造血模型方程的数值解数据,对此广义模型与现有的ＲＢＦ模型和梯度径向基函数（ＧＲＢＦ）模型对一笥时间序列预测问题的应用结果进行了比较与讨论,显示出这种广义模型的应用有效性。     

将隐含Markov模型应用于杂波中的雷达检测

通常将复合随机变量Ｚ＝ＡＸ作为海杂波幅度的模型，其中Ａ为正值的随机变量，Ｘ具有瑞利分布。Ｋ和离散瑞利混合分布源自采用Ａ分别为伽马或离散分布的该模型。在某些应用中，能将Ａ的连续值进行相关。如果该相关被模拟成有限Ｍａｒｋｏｖ过程的话，那么用隐含Ｍａｒｋｏｖ模型（ＨＭＭ）来描述Ｚ。仅有幅度的和相位相干检测的统计结果是由ＨＭＭ模型用局部最优和似然比技术导出的。文中将这些算法的性能与使用雷达数据的ＣＦＡＲ和多普勒处理器作了比较。     

HMM-WC-FNM模型在触头表面形貌去噪中的应用

本文提出了一种可靠的图像去噪算法,基于观察图像是期望图像叠加了不规则噪声的假设,用有限高斯混合分布(FNM)描述期望图像分解小波系数(WC)的先验分布,用隐马尔可夫模型(HMM)描述同一方向不同分解级之间的小波系数的依赖关系,采用Bayes准则,根据期望图像的后验分布(以观测图像为条件)所对应的HMM模型的条件概率,用EM(expectation maximization)优化算法,获得MAP(maximization a posteriori)准则下的去噪图像。针对银基触头材料表面形貌去噪对几种算法作定性比较,并对去噪性能给出定量分析,仿真结果表明,此方法有效去除噪声的同时,能保留原始图像的细节信息。     

Maximum a posteriori spatial probability segmentation

An image segmentation algorithm that performs pixel-by-pixel segmentation on an image with consideration of the spatial information is described. The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probability that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a posteriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such that each pixel is segmented into a particular class when the a posteriori spatial probability is a maximum. The proposed segmentation algorithm is implemented in an iterative form. During the iteration, a series of intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure is chosen as the optimum segmented image. Results from segmenting synthetic and practical images demonstrate that the MASP algorithm is capable of achieving better results when compared with other global thresholding methods     

Statistical approach to segmentation of single-channel cerebral MRimages

A statistical model is presented that represents the distributions of major tissue classes in single-channel magnetic resonance (MR) cerebral images. Using the model, cerebral images are segmented into gray matter, white matter, and cerebrospinal fluid (CSF). The model accounts for random noise, magnetic field inhomogeneities, and biological variations of the tissues. Intensity measurements are modeled by a finite Gaussian mixture. Smoothness and piecewise contiguous nature of the tissue regions are modeled by a three-dimensional (3-D) Markov random field (MRF). A segmentation algorithm, based on the statistical model, approximately finds the maximum a posteriori (MAP) estimation of the segmentation and estimates the model parameters from the image data. The proposed scheme for segmentation is based on the iterative conditional modes (ICM) algorithm in which measurement model parameters are estimated using local information at each site, and the prior model parameters are estimated using the segmentation after each cycle of iterations. Application of the algorithm to a sample of clinical MR brain scans, comparisons of the algorithm with other statistical methods, and a validation study with a phantom are presented. The algorithm constitutes a significant step toward a complete data driven unsupervised approach to segmentation of MR images in the presence of the random noise and intensity inhomogeneities     

Bayesian decision feedback for segmentation of binary images

We present real-time algorithms for the segmentation of binary images modeled by Markov mesh random fields (MMRFs) and corrupted by independent noise. The goal is to find a recursive algorithm to compute the maximum a posteriori (MAP) estimate of each pixel of the scene using a fixed lookahead of D rows and D columns of the observations. First, this MAP fixed-lag estimation problem is set up and the corresponding optimal recursive (but computationally complex) estimator is derived. Then, both hard and soft (conditional) decision feedbacks are introduced at appropriate stages of the optimal estimator to reduce the complexity. The algorithm is applied to several synthetic and real images. The results demonstrate the viability of the algorithm both complexity-wise and performance-wise, and show its subjective relevance to the image segmentation problem.     

Segmentation of Gabor-filtered textures using deterministicrelaxation

A supervised texture segmentation scheme is proposed in this article. The texture features are extracted by filtering the given image using a filter bank consisting of a number of Gabor filters with different frequencies, resolutions, and orientations. The segmentation model consists of feature formation, partition, and competition processes. In the feature formation process, the texture features from the Gabor filter bank are modeled as a Gaussian distribution. The image partition is represented as a noncausal Markov random field (MRF) by means of the partition process. The competition process constrains the overall system to have a single label for each pixel. Using these three random processes, the a posteriori probability of each pixel label is expressed as a Gibbs distribution. The corresponding Gibbs energy function is implemented as a set of constraints on each pixel by using a neural network model based on Hopfield network. A deterministic relaxation strategy is used to evolve the minimum energy state of the network, corresponding to a maximum a posteriori (MAP) probability. This results in an optimal segmentation of the textured image. The performance of the scheme is demonstrated on a variety of images including images from remote sensing.     

基于Wishart分布和MRF的多视全极化SAR图像分割

提出一种新的多视全极化SAR图像分割方法.将描述多视协方差矩阵的Wishart分布与马尔可夫随机场模型结合起来,利用迭代条件模型法(ICM)求取最大后验概率准则下的分割结果,其中ICM所需的初始分割图由基于Wishart分布的最大似然法获得.NASA/JPL实验室AIRSAR系统多视全极化数据的实验结果表明,与几种常用方法相比,本文方法分割精度更高,分割结果图中孤立像素少,图像连通性好.     

一种新的基于时空马尔可夫随机场的运动目标分割技术

在图像处理领域，视频图像序列中的运动目标分割技术是一个被广泛研究的热点课题。该文提出一种新的基于时空马尔可夫随机场的运动目标分割技术。首先，对视频序列的前后3帧图像进行处理，获得两帧初始标记场；随后，对两帧初始标记场进行“与”操作，获得共同标记场；最后，以原始图像的色彩聚类图像作为先验知识，重新定义Gibbs能量函数，并利用迭代条件模型（ICM）实现最大后验概率（MAP）的估算问题，获得优化标记场。实验结果表明：该模型克服了传统时穿马尔可夫随机场模型因运动产生的晶露遮挡现象，同时减弱了运动一致性造成的空洞现象并削弱了噪声的影响。     

A MAP approach for joint motion estimation, segmentation, and super resolution.

Super resolution image reconstruction allows the recovery of a high-resolution (HR) image from several low-resolution images that are noisy, blurred, and down sampled. In this paper, we present a joint formulation for a complex super-resolution problem in which the scenes contain multiple independently moving objects. This formulation is built upon the maximum a posteriori (MAP) framework, which judiciously combines motion estimation, segmentation, and super resolution together. A cyclic coordinate descent optimization procedure is used to solve the MAP formulation, in which the motion fields, segmentation fields, and HR images are found in an alternate manner given the two others, respectively. Specifically, the gradient-based methods are employed to solve the HR image and motion fields, and an iterated conditional mode optimization method to obtain the segmentation fields. The proposed algorithm has been tested using a synthetic image sequence, the "Mobile and Calendar" sequence, and the original "Motorcycle and Car" sequence. The experiment results and error analyses verify the efficacy of this algorithm.     

视频对象分割中基于Gibbs随机场模型的空分割结合方法

本文提出了一种基于Gibbs随机场模型的时空分割结合方法,用于视频对象的分割.该方法为每一帧图像的分割模板建立Gibbs随机场模型,将时间域分割结果作为初始标记场,空间域的分割结果作为一个图像观察场,然后利用Gibbs模型的约束条件将二者结合起来,得到该帧最后的分割标记场.实验结果表明,这种时空结合方法可以较好地避免以往的比重法过分依赖于空间域分割精度的问题.     

视频对象分割中基于Gibbs随机场模型的时空分割结合方法

本文提出了一种基于Gibbs随机场模型的时空分割结合方法 ,用于视频对象的分割 .该方法为每一帧图像的分割模板建立Gibbs随机场模型 ,将时间域分割结果作为初始标记场 ,空间域的分割结果作为一个图像观察场 ,然后利用Gibbs模型的约束条件将二者结合起来 ,得到该帧最后的分割标记场 .实验结果表明 ,这种时空结合方法可以较好地避免以往的比重法过分依赖于空间域分割精度的问题 .