PVC建材表面缺陷检测系统研究与设计

为满足PVC建材表面缺陷在线系统高速、高分辨率的检测要求,提出PVC建材表面缺陷检测的系统设计方案。为保证检测系统的实时性,硬件上采用面阵CCD摄像机与LED面光源相结合的图像采集装置,软件上采用实时处理和准实时处理相结合的图像处理方法。针对缺陷图像高噪声、直方图单峰明显的特征,提出边缘检测分割技术与数学形态处理相结合的算法流程。通过分析图像缺陷的统计特性,提取PVC建材表面缺陷区域的特征参数,利用基于决策树的分类器将缺陷分为气泡、色痕、裂痕等。实验结果验证算法的有效性和实时性,其缺陷的检出率达到95%以上,识别率达90%以上。     

亚像素边缘检测算法在刀具参数检测中的应用

传统边缘检测算法定位精度低、效率低并且对噪声比较敏感,已经不能满足工业生产的需求.基于此,本文提出了基于拟合的亚像素边缘检测算法--五次多项式拟合亚像素边缘检测算法.该算法首先在待测边缘附近取一系列的点,求得这些点的灰度值,通过五次多项式曲线对灰度曲线进行拟合,求得五次多项式的二阶导数为零点即为亚像素位置.并对五次多项...     

基于小波变换的自适应梯度边缘检测算法

针对传统的单一边缘检测算法抗噪能力差、边缘不连续等不足,本文提出采用两种算法相结合的方式来进行边缘检测。首先,对原始图像进行多层小波分解;然后,对小波分解后的图像低频部分用提出的8点邻域自适应梯度算法进行边缘检测,依靠边缘生长方法保证检测出的边缘的连续性,对高频部分用小波变换的局部模极大值算法检测图像的边缘;最后,将各层边缘信息按一定的融合规则融合起来得到最终的图像边缘。实验结果表明,该方法与传统的边缘检测算法相比具有定位精度高、去噪效果好等明显的优点,也能较准确地提取图像的边缘。     

无监督名片区域自动提取算法研究

随着商务活动日趋频繁,商务名片管理的信息化日益受到人们的重视,其中名片区域的定位是名片图像预处理的关键步骤。本文提出一种利用边缘图像定位名片区域的算法,该算法比常用的聚类算法运算量要小,且对倾斜、展缩等具有一定鲁棒性。     

基于拓扑导数的复扩散在图像去噪及边缘提取中的应用

提出了基于拓扑导数的非线性复扩散用于图像去噪及边缘提取的一种算法.由于线性扩散会使图像边缘模糊,基于拓扑优化思想,对每个像素点的线性复扩散系数扰动,使得拓扑导数最小的扩散系数为最优.文中选取的扩散系数具有各向异性的特性,从而克服了Perona-Malik的各向同性扩散系数不利于去除边缘噪声的缺陷,选择拓扑导数足够小的像素点,对这些像素点用最优扩散系数进行扩散.文中给出了使算法迭代终止的判据.实验证明,与Guy Gilboa的非线性复扩散相比,本文方法对原始加噪图像处理后,实部图像体现出了更好的去噪效果,虚部图像则很好地保留了图像边缘,此外,本文方法还消除了Perona-Malik的方法对图像去噪后产生的阶梯效应.     

A privacy preserving technique for distance-based classification with worst case privacy guarantees

There has been relatively little work on privacy preserving techniques for distance based mining. The most widely used ones are additive perturbation methods and orthogonal transform based methods. These methods concentrate on privacy protection in the average case and provide no worst case privacy guarantee. However, the lack of privacy guarantee makes it difficult to use these techniques in practice, and causes possible privacy breach under certain attacking methods. This paper proposes a novel privacy protection method for distance based mining algorithms that gives worst case privacy guarantees and protects the data against correlation-based and transform-based attacks. This method has the following three novel aspects. First, this method uses a framework to provide theoretical bound of privacy breach in the worst case. This framework provides easy to check conditions that one can determine whether a method provides worst case guarantee. A quick examination shows that special types of noise such as Laplace noise provide worst case guarantee, while most existing methods such as adding normal or uniform noise, as well as random projection method do not provide worst case guarantee. Second, the proposed method combines the favorable features of additive perturbation and orthogonal transform methods. It uses principal component analysis to decorrelate the data and thus guards against attacks based on data correlations. It then adds Laplace noise to guard against attacks that can recover the PCA transform. Third, the proposed method improves accuracy of one of the popular distance-based classification algorithms: K-nearest neighbor classification, by taking into account the degree of distance distortion introduced by sanitization. Extensive experiments demonstrate the effectiveness of the proposed method.     

A Numerical Study of Diagonally Split Runge–Kutta Methods for PDEs with Discontinuities

Diagonally split Runge–Kutta (DSRK) time discretization methods are a class of implicit time-stepping schemes which offer both high-order convergence and a form of nonlinear stability known as unconditional contractivity. This combination is not possible within the classes of Runge–Kutta or linear multistep methods and therefore appears promising for the strong stability preserving (SSP) time-stepping community which is generally concerned with computing oscillation-free numerical solutions of PDEs. Using a variety of numerical test problems, we show that although second- and third-order unconditionally contractive DSRK methods do preserve the strong stability property for all time step-sizes, they suffer from order reduction at large step-sizes. Indeed, for time-steps larger than those typically chosen for explicit methods, these DSRK methods behave like first-order implicit methods. This is unfortunate, because it is precisely to allow a large time-step that we choose to use implicit methods. These results suggest that unconditionally contractive DSRK methods are limited in usefulness as they are unable to compete with either the first-order backward Euler method for large step-sizes or with Crank–Nicolson or high-order explicit SSP Runge–Kutta methods for smaller step-sizes. We also present stage order conditions for DSRK methods and show that the observed order reduction is associated with the necessarily low stage order of the unconditionally contractive DSRK methods. The work of C.B. Macdonald was partially supported by an NSERC Canada PGS-D scholarship, a grant from NSERC Canada, and a scholarship from the Pacific Institute for the Mathematical Sciences (PIMS). The work of S. Gottlieb was supported by AFOSR grant number FA9550-06-1-0255. The work of S.J. Ruuth was partially supported by a grant from NSERC Canada.     

Multivariate statistical process control based on multiway locality preserving projections

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling’s T² (D) and the squared prediction error (SPE or Q) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data.     

基于粗糙集理论的图像平滑算法研究

基于粗糙集理论,针对高斯噪声和脉冲噪声,分别采用高斯模板和中值滤波技术,提出了图像平滑算法.这两种算法在去噪的同时,都能够很好地保持图像细节,并且简单易行、处理速度快、使用范围广.通过实验,该算法对灰度图像和彩色图像的处理效果较之传统的处理方法,质量上有较大的提高.     

基于MATLAB的数字图像处理

介绍了数字图像处理技术和MATLAB的图像处理工具箱函数,运用MATLAB语言实现图像的直方图均衡化和边缘检测。