基于小波变换和矢量量化的人脸图像压缩

提出一种新的在小波域内进行矢量量化的算法.该算法在对图像进行多级小波变换后,构造三个方向的跨频带矢量,同时采用分类矢量量化,非线性插补矢量量化和基于人眼视觉特性的加权矢量量化,提高了图像的编码效率和重构质量.仿真结果表明,该算法实现简单,在较低的编码率下,可达到较好的压缩效果.     

梯形高斯隶属度量化算法及在汽车图像库的仿真

为了提高汽车图像检索的准确率,选择了一个合适的颜色空间和量化算法。首先本文自建了1000张汽车图像库,其次在HSV颜色空间提出了梯形高斯隶属度量化算法进行特征提取,最后通过欧式距离和加权距离进行相似度匹配。实验结果表明,该算法对汽车图像进行检索时的平均查准率要比梯形隶属度量化算法在欧氏距离进行相似度量时平均高出11.2个百分点;在加权距离进行相似度量时平均高出10.9个百分点。     

基于分类学习字典全局稀疏表示模型的图像修复算法研究

基于稀疏重构的图像修复依赖于图像全局自相似性信息的利用和稀疏分解字典的选择,为此提出了基于分类学习字典全局稀疏表示模型的图像修复思路.该算法首先将图像未丢失信息聚类为具有相似几何结构的多个子区域,并分别对各个子区域用K-SVD字典学习方法得到与各子区域结构特征相适应的学习字典.然后根据图像自相似性特点构建能够描述图像块空间组织结构关系的全局稀疏最大期望值表示模型,迭代地使用该模型交替更新图像块的组织结构关系和损坏图像的估计直到修复结果趋于稳定.实验结果表明,方法对于图像的纹理细节、结构信息都能起到好的修复作用.     

基于覆盖的精度与程度逻辑或粗糙集模型

精度与程度是两个重要的量化指标,它们的结合双量化具有完备意义。本文将精度与程度进行复合,研究基于覆盖的精度与程度逻辑或粗糙集模型。首先,把等价类扩展为覆盖,利用元素的最小描述,提出基于覆盖的精度与程度逻辑或粗糙集模型;其次,给出该模型的上下近似、正域、负域及边界的定义,讨论一些相关的性质和定理;最后,实例分析验证了该模型的优越性。该模型更好地拓展了变精度覆盖粗糙集模型和程度覆盖粗糙集模型,对粗糙集区域的刻画更加准确。     

基于Huber正则化的红外与可见光图像融合

图像融合通常是指从多源信道采集同一目标图像,将互补的多焦点、多模态、多时相和/或多视点图像集成在一起,形成新图像的过程.在本文中,我们采用基于Huber正则化的红外与可见光图像的融合模型.该模型通过约束融合图像与红外图像相似的像素强度保持热辐射信息,以及约束融合图像与可见光图像相似的灰度梯度和像素强度保持图像的边缘和纹理等外观信息,同时能够改善图像灰度梯度相对较小区域的阶梯效应.为了最小化这种变分模型,我们结合增广拉格朗日方法(ALM)和量身定做有限点方法(TFPM)的思想设计数值算法,并给出了算法的收敛性分析.最后,我们将所提模型和算法与其他七种图像融合方法进行定性和定量的比较,分析了本文所提模型的特点和所提数值算法的有效性.     

量化转换系统的格值语言包含关系

近十年来,量化形式化验证方法的研究取得了很多的研究成果。量化转换系统是一种新型的量化模型,该模型的主要特点是其动作集合上被赋予一个基于完备剩余格的格值等价关系。在量化转换系统的模型上,本文提出了一种格值语言包含关系去度量系统的一个状态所接受的语言能在多大程度上被另一个状态所接受的语言所包含,研究了这种关系的计算复杂性问题,并用格值版本的HennessyMilner逻辑的子逻辑提供了它的逻辑刻画。所有这些性质表明所提出的格值语言包含关系为并发和分布式系统的量化验证提供了重要的理论基础。     

基于MCMC算法的时变Copula-GARCH-M-t模型及组合风险预测

为了准确地量化资产之间的时变相依结构和预测组合风险,本文考虑到投资者对资产风险偏好的差异,假设资产收益率序列的新息服从标准t分布,提出时变Copula-GARCH-M-t模型,推导了模型参数的两步MCMC估计方法,还得到了组合风险(VaR和CVaR)的一步预测方法。最后选取上证综合指数和标准普尔500指数,验证了所提模型及方法的可行性和优越性,同时该模型较为准确地量化了两指数在次贷危机后的时变相依结构特征。     

基于MCLP的知识挖掘模型在项目管理中的应用

针对项目管理中不同知识的分类和表达模式,提出了一种基于多目标线性规划的知识挖掘模型.该模型在对文本信息预处理的基础上,采用多目标线型规划方法,实现了对可量化信息和不可量化信息的同时挖掘,从而挖掘出更准确的项目知识模式,实现了项目管理中的知识的再利用,并就应用过程中遇到的具体问题进行了探讨.     

序信息系统中的双量化决策粗糙集模型

决策粗糙集和程度粗糙集作为两类广义的粗糙集模型,分别从相对信息量化和绝对信息量化的观点对经典的粗糙集模型进行了扩张。本文在序信息系统中将这两类广义粗糙集模型进行融合,即将决策粗糙集和程度粗糙集的近似算子重新组合,以此构造了两种不同类型的双量化粗糙集模型。然后,对模型的一些基本性质进行了讨论,新建立的粗糙集模型包含了相对量化信息和绝对量化信息。最后,通过对实际案例分析展现了双量化决策规则的获取方法,结果表明两类模型均可以有效的获取决策规则,本文为序信息系统中的决策分析提供了新的选择。     

随机森林在量化选股中的应用研究

通过分析国内外量化选股模型采用的指标体系,从焦健的六因子模型出发,使用指标相关性分析方法,提出了八因子选股模型指标体系,选用了2013年3月200只股票的样本数据,使用随机森林算法实现了对2013年4月股票涨跌情况较高精确度的预测,通过对比分析焦健的六因子模型,并分析优选后的股票在行业平均收益、最值方面的实际表现,验证了该量化选股模型在中国股票市场上有较好的性能。     

图像处理的结构层次建模

图像处理是应用广泛的现代工程技术 ,但直到现在尚无完备的数学模型体系 .依结构层次观点对其数学模型化进行探索 ,给出了图像处理的结构层次模型 ,将图像处理与数学科学中的常见数学理论联系起来 .同时 ,对目前常见图像处理器进行数学模型描述 .     

Detailed computational and experimental fluid dynamics of fluidized beds

To describe the hydrodynamic phenomena prevailing in large industrial scale fluidized beds continuum models are required. The flow in these systems depends strongly on particle–particle interaction and gas–particle interaction. For this reason, proper closure relations for these two interactions are vital for reliable predictions on the basis of continuum models. Gas–particle interaction can be studied with the use of the lattice Boltzmann model (LBM), while the particle–particle interaction can suitably be studied with a discrete particle model. In this work it is shown that the discrete particle model, utilizing a LBM based drag model, has the capability to generate insight and eventually closure relations in processes such as mixing, segregation and homogeneous fluidization.     

A variational formulation for physical noised image segmentation

Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to structural units in the scene or objects of interest. The proposed region-based model uses total variation as a regularization term, and different fidelity term can be used for image segmentation in the cases of physical noise, such as Gaussian, Poisson and multiplicative speckle noise. Our model consists of five weighted terms, two of them are responsible for image denoising based on fidelity term and total variation term, the others assure that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. We also develop a primal-dual hybrid gradient algorithm for our model. Numerical results on various synthetic and real images are provided to compare our method with others,these results show that our proposed model and algorithms are effective.     

二值形态运算的感知器模型及其应用

给出了二值形态运算模型与感知器模型之间的关系.以此为基础对传统的二值数学形态学基本运算进行了推广,建立了基于感知器的形态运算模型.新的模型为传统的二值形态运算赋予了可调节机制.计算机模拟实验表明该方法较传统的二值形态运算有效.     

A Fast Augmented Lagrangian Method for Euler's Elastica Models

In this paper, a fast algorithm for Euler's elastica functional is proposed, in which the Euler's elastica functional is reformulated as a constrained minimization problem. Combining the augmented Lagrangian method and operator splitting techniques, the resulting saddle-point problem is solved by a serial of subproblems. To tackle the nonlinear constraints arising in the model, a novel fixed-point-based approach is proposed so that all the subproblems either is a linear problem or has a closed-form solution. We show the good performance of our approach in terms of speed and reliability using numerous numerical examples on synthetic, real-world and medical images for image denoising, image inpainting and image zooming problems.     

Improved variational image registration model and a fast algorithm for its numerical approximation

In a multimodal image registration scenario, where two given images have similar features, but noncomparable intensity variations, the sum of squared differences is not suitable for inferring image similarities. In this article, we first propose a new variational model based on combining intensity and geometric transformations, as an alternative to use mutual information and an improvement to the work by Modersitzki and Wirtz (Modersitzki and Wirtz, Lect Notes Comput Sci 4057 (2006), 257–263), and then develop a fast multigrid (MG) algorithm for solving the underlying system of fourth‐order and nonlinear partial differential equations. We can demonstrate the effective smoothing property of the adopted primal‐dual smoother by a local Fourier analysis. Numerical tests will be presented to show both the improvements achieved in image registration quality and MG efficiency. © 2011 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2011     

On a New Diffeomorphic Multi-Modality Image Registration Model and Its Convergent Gauss-Newton Solver

In this work, we propose a new variational model for multi-modal image registration and present an efficient numerical implementation. The model minimizes a new functional based on using reformulated normalized gradients of the images as the fidelity term and higher-order derivatives as the regularizer. A key feature of the model is its ability of guaranteeing a diffeomorphic transformation which is achieved by a control term motivated by the quasi-conformal map and Beltrami coefficient. The existence of the solution of this model is established. To solve the model numerically, we design a Gauss-Newton method to solve the resulting discrete optimization problem and prove its convergence; a multilevel technique is employed to speed up the initialization and avoid likely local minima of the underlying functional. Finally, numerical experiments demonstrate that this new model can deliver good performances for multi-modal image registration and simultaneously generate an accurate diffeomorphic transformation.     

On weak solutions for an image denoising-deblurring model

A new denoising-deblurring model in image restoration is proposed,in which the regularization term carries out anisotropic diffusion on the edges and isotropic diffusion on the regular regions.The existence and uniqueness of weak solutions for this model are proved,and the numerical model is also testified.Compared with the TV diffusion,this model preferably reduces the staircase appearing in the restored images.     

基于PSO_Trainlm BP模型的图像去噪研究

针对在使用BP模型进行图像去噪时,模型存在的对初始权阈值敏感、易陷入局部极小值和收敛速度慢的问题.为了提高模型去噪效率,提出采用改进粒子群神经网络模型进行图像去噪.首先运用改进粒子群算法对BP神经网络权阈值进行初始寻优,再用trainlm BP算法对优化的网络权阈值进一步精确优化,随后建立基于粒子群算法的BP神经网络去噪模型,并将其应用到图像去噪研究中.仿真结果表明,新模型结合了粒子群算法的全局寻优能力和BP算法的局部搜索能力,减小了模型对初始权阈值的敏感性,有效防止了模型陷入局部极小值的可能,提高了图像去噪模型的速度和质量.     

利用分裂Bregman方法的图像恢复组合模型

ROF模型是图像恢复中的经典模型,具有保留图像边缘的优点,但同时也存在梯子现象.而利用二次范数fΩ｜▽u｜2 dxdy的模型可以避免梯子现象,但容易使图像变得模糊.针对两种方法的优缺点,提出了一种新的通过设置边缘检测开关函数的组合模型,在图像平坦区利用二次范数模型处理,而在强边缘处利用ROF模型处理,而且应用分裂的Br...