遗传模糊聚类算法在图像边缘检测中的应用

将一种改进的遗传模糊c-均值聚类(GFGA)算法应用到图像的边缘检测中.我们将灰度图像中的每一个像素点看成是一个数据样本,将该点的灰度值经过Robert算子、Sobel算子和Prewitt算子处理构成它的特性向量,形成具有三维特征的数据集,然后对这个数据集应用遗传模糊聚类算法进行分类,自适应地检测出图像的边缘点,达到提取边缘的目的.实验结果表明,这种混合算法能得到很好的边缘效果,并且得到的结果无需再细化处理,提高了边缘定位的精度.     

用于边缘检测的Laplace——样条(▽2S)算子

根据数据平滑理论,提出了一种图象边缘检测的方法:把二维四阶B样条函数与Laplacian算子相结合组合成▽²S算子,对图象卷积之后,根据过零点(ZeroCrossing)确定图象边缘。文中证明了它在图象平滑和边缘检测效果上,优于Marr和Hildreth提出的▽²G算子,且计算量较小。而▽²G算子可用高阶▽²S算子近似。文中给出了一些实验结果。     

基于方向信息测度的图像边缘检测

边缘检测是图像处理中—个重要的研究课题．针对传统图像边缘检测算法对噪声敏感的问题,本文在分析图像像素灰度信息的基础上,建立了—个改进的确定方向信息测度的方法,并利用震动滤波对边缘检测图像进行增强,该方法在滤除噪声的同时,能有效地保留图像的基本目标信息,正确提取图像的边缘．     

基于改进的变分水平集C-V图像分割模型研究

为解决C-V模型中弱边缘或边缘模糊图像分割问题,提出了用边缘停止函数代替正则化Dirac函数的C-V图像分割模型.首先对正则化Heaviside函数和正则化Dirac函数中的参数进行了讨论,然后利用图像边缘信息将梯度算子引入正则化Driac函数中,对C-V模型进行改进,最后,用边缘停止函数代替C-V模型中的正则化Dirac函数.实验结果显示,提出的模型比C-V模型对图像的分割效果更好.     

改进Canny算法在钢印数字特征提取中的应用

边缘检测是实现图像分割、特征提取和图像理解的基础.研究了传统Canny算子的优势与不足.在此基础上,提出了一种快速分块自适应Canny算法.方法首先按字符大小分割图像,然后在每一块上进行自适应边缘检测.自适应边缘检测是在平滑图像的同时得到高斯滤波尺度参数,然后采用Otsu方法的自适应阈值计算Canny算子的高、低门限值.实验结果表明,方法不需人工设定参数就能自动提取不同光照背景下的钢印数字边缘,而且能有效抑制噪声,与传统Canny算子相比,边缘连接程度最佳,噪声敏感程度较低,实时性较强.     

彩色图象边缘检测的新算法——广义模糊算子法

给出了广义模糊集合的定义,并提出用一个广义模糊算子将广义模糊集合变换成普通模糊集合,从而为二维图象边缘检测的最新算法——广义模糊算子法奠定了充分的理论依据.实践表明,这是一个简单、快速且定位精度很高的算法;此外,文中还就彩色图象的边缘检测问题,提出了具体的实施步骤.     

三三次长方体有限元的超收敛

本文首先介绍了三维投影型插值算子,并通过这个算子导出了三三次长方体有限元的弱估计．然后,利用离散导数Green函数的W^2,1半范估计和弱估计证明了有限元uh的梯度和三三次投影型插值Пh^2u的梯度在逐点意义下有超逼近．最后,将这种超逼近用于超收敛分析并导出了有限元的整体超收敛估计．     

基于Canny算子边缘检测的小波阈值去噪方法

本文研究了信号处理中图像去噪的问题.利用小波变换理论提出了一种基于Canny算子边缘检测的小波阈值去噪方法,实验结果表明,该方法在有效去除噪声的同时能够更好地保留图像的边缘.     

图像的多层次模糊增强与边缘提取

在单层次模糊增强算法中,某个灰度层次附近的边缘得到了增强,同时其它的一些边缘受到了抑制。为了同时增强图像中不同灰度层次的边缘信息,提出一种多层次模糊增强算法,并且给出了快速实现算法。此外,把多层次模糊增强算法应用于图像边缘提取中,取得了优于单层次模糊增强的效果。     

多目标最优化问题相依导数的灵敏性与稳定性(英)

设Y（u）是可行域在目标空间的像，W（u）＝minpY（u）是有效集和N（u）＝W－minDDY是弱有效集．在适当条件下，本文证明了DNW－minpY和W－minDDY＝DW＝DN．本文也讨论了边缘映射导数的连续性．     

Affine Invariant Detection: Edge Maps, Anisotropic Diffusion, and Active Contours

In this paper we undertake a systematic investigation of affine invariant object detection and image denoising. Edge detection is first presented from the point of view of the affine invariant scale-space obtained by curvature based motion of the image level-sets. In this case, affine invariant maps are derived as a weighted difference of images at different scales. We then introduce the affine gradient as an affine invariant differential function of lowest possible order with qualitative behavior similar to the Euclidean gradient magnitude. These edge detectors are the basis for the extension of the affine invariant scale-space to a complete affine flow for image denoising and simplification, and to define affine invariant active contours for object detection and edge integration. The active contours are obtained as a gradient flow in a conformally Euclidean space defined by the image on which the object is to be detected. That is, we show that objects can be segmented in an affine invariant manner by computing a path of minimal weighted affine distance, the weight being given by functions of the affine edge detectors. The gradient path is computed via an algorithm which allows to simultaneously detect any number of objects independently of the initial curve topology. Based on the same theory of affine invariant gradient flows we show that the affine geometric heat flow is minimizing, in an affine invariant form, the area enclosed by the curve.     

On algebraic connectivity as a function of an edge weight

We consider the Laplacian matrix of a weighted graph, and how the algebraic connectivity, α, behaves when considered as a function of a single edge weight. Under suitable differentiability conditions, we bound the first derivative of α from above, show that α is necessarily concave down, and produce a lower bound on the second derivative of α. When α is simple, we discuss the effect of increasing an edge weight on the corresponding Fiedler vector. We also compute the limiting value of α as the edge weight increases to infinity.     

Displacement-based finite difference approximations of derivatives of the tangent stiffness matrix with respect to the load parameter

The vehicle to investigate to which extent energy-based categorization of buckling can be linked up with spherical geometry is the so-called consistently linearized eigenproblem. This investigation requires computation of the first and the second derivative of the tangent stiffness matrix with respect to a dimensionless load parameter λ in the frame of the Finite Element Method (FEM). A finite-difference approximation of the first derivative of , redefined as a directional derivative, has proved to meet the requirements of computational efficiency and sufficient accuracy. It represents a displacement-based finite-difference approximation, abbreviated as DBFDA. The present work is devoted to the computation of a DBFDA of the second derivative of ˜ KT with respect to λ. For the special case of a two-dimensional co-rotational beam element, an analytical solution of this derivative is presented. A circular arch, subjected to a vertical point load on its apex, serves as an example for numerically assessing the usefulness of the computed DBFDAs of the first and the second derivative of with respect to λ. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

The Geometry of Systems of Third Order Differential Equations Induced by Second Order Regular Lagrangians

A system of third order differential equations, whose coefficients do not depend explicitly on time, can be viewed as a third order vector field, which is called a semispray, and lives on the second order tangent bundle. We prove that a regular second order Lagrangian induces such a semispray, which is uniquely determined by two associated Poincaré-Cartan one-forms. To study the geometry of this semispray, we construct a horizontal distribution, which is a Lagrangian subbundle for an associated Poincaré-Cartan two-form. Using this semispray and the associated nonlinear connection we define dynamical covariant derivatives of first and second order. With respect to this, the second order dynamical derivative of the Lagrangian metric tensor vanishes.     

均衡约束为二阶锥约束广义方程的数学规划问题的二阶充分条件(英)

本文考虑一类均衡约束为二阶锥约束广义方程的数学规划问题. 我们通过一个非光滑映射的方向导数, 给出了临界锥的定义, 并建立它在可行点处的等价形式. 基于此临界锥, 我们提出了均衡约束为二阶锥约束广义方程的数学规划问题的二阶充分性条件, 并且验证了在适当的条件下, M-稳定点处的二阶充分性条件是二阶增长条件成立的充分条件.     

Closed-form expressions for the finite difference approximations of first and higher derivatives based on Taylor series

Numerical differentiation formulas based on interpolating polynomials, operators and lozenge diagrams can be simplified to one of the finite difference approximations based on Taylor series. In this paper, we have presented closed-form expressions of these approximations of arbitrary order for first and higher derivatives. A comparison of the three types of approximations is given with an ideal digital differentiator by comparing their frequency responses. The comparison reveals that the central difference approximations can be used as digital differentiators, because they do not introduce any phase distortion and their amplitude response is closer to that of an ideal differentiator. It is also observed that central difference approximations are in fact the same as maximally flat digital differentiators. In the appendix, a computer program, written in MATHEMATICA is presented, which can give the approximation of any order to the derivative of a function at a certain mesh point.     

Generalized Rough Polyharmonic Splines for Multiscale PDEs with Rough Coefficients

We demonstrate the construction of generalized Rough Polyharmonic Splines (GRPS) within the Bayesian framework, in particular, for multiscale PDEs with rough coefficients. The optimal coarse basis can be derived automatically by the randomization of the original PDEs with a proper prior distribution and the conditional expectation given partial information on, for example, edge or first order derivative measurements as shown in this paper. We prove the (quasi)-optimal localization and approximation properties of the obtained bases. The basis with respect to edge measurements has first order convergence rate, while the basis with respect to first order derivative measurements has second order convergence rate. Numerical experiments justify those theoretical results, and in addition, show that edge measurements provide a stabilization effect numerically.     

多复变数的Schwarz导数(Ⅴ)

本文从Thurston的观点出发,用二阶逼近来定义与讨论矩阵空间C~(m×n)（m≤n）中的域上全纯映照的Schwarz导数及高阶Schwarz导数,证明：如果它们存在的话,那么它们是在R_I（m,n）的紧对偶空间CG（m,n）的全纯自同构群下的相似不变量．并证明：这样得到的Schwarz导数与前几文［1－4］中由Ahlfors的观点得到的Schwarz导数是相一致的．此外,还应用这种观点定义与讨论了C~N中的域上全纯映照的Schwarz导数．     

一类Banach空间中的分析性质及反例

本文对具有Schauder基的无穷维Banach空间上的映射定义偏导数,并讨论f的可徽性与偏导数存在并连续的关系;同时,本文还时论象所在空间为具有Schauder基的空间时,映射f与坐标映射f_i在可微性、连续性方面的关系.     

A fast level set model of multi‐atlas labels fusion for 3D MRI tissues segmentation with application of split Bregman method

In this paper, we propose a new fast level set model of multi‐atlas labels fusion for 3D magnetic resonance imaging (MRI) tissues segmentation. The proposed model is aimed at segmenting regions of interest in MR images, especially the tissues such as the amygdala, the caudate, the hippocampus, the pallidum, the putamen, and the thalamus. We first define a new energy functional by taking full advantage of an image data term, a length term, and a label fusion term. Different from using the region‐scalable fitting image data term and length term directly, we define a new image data term and a new length term, which is also incorporated with an edge detect function. By introducing a spatially weight function into the label fusion term, segmentation sensitivity to warped images can be largely improved. Furthermore, the special structure of the new energy functional ensures the application of the split Bregman method, which is a significant highlight and can improve segmentation efficiency of the proposed model. Because of these promotions, several good characters, such as accuracy, efficiency, and robustness have been exhibited in experimental results. Quantitative and qualitative comparisons with other methods have demonstrated the superior advantages of the proposed model.