基于图像传感器的圆心定位技术实用化研究

基于图像传感器的圆心定位技术适用于多种非接触式测量系统。这类技术对测量环境的要求比较苛刻。以提高圆心定位技术的适应能力为目的,提出了针对抗干扰、边界识别和圆心拟合等测量过程关键技术环节的实用化改进方案,综合运用了时间递推滤波、深度优先搜索算法和迭代最小二乘法等方法。改进后的测量系统具有良好的稳定性和适应性,在地铁隧道形变监测项目中成功投入使用。     

Characterizing Strong Stability Preserving Additive Runge-Kutta Methods

Space discretization of some time dependent partial differential equations give rise to ordinary differential equations containing additive terms with different stiffness properties. In these situations, additive Runge-Kutta (additive RK) methods are used. For additive RK methods the curve of absolute monotonicity gives stepsize restrictions for monotonicity. Necessary conditions for nontrivial curves of absolute monotonicity are the nonnegativity of the additive RK coefficients and some inequalities on some incidence matrices. In this paper we characterize strong stability preserving additive Runge-Kutta methods giving some order barriers and structural properties. Research supported by the Ministerio de Educación y Ciencia, Project MTM2005-03894.     

3D Target Scale Estimation and Target Feature Separation for Size Preserving Tracking in PTZ Video

To achieve size preserving tracking, in addition to controlling the camera’s pan and tilt motions to keep the object of interest in the camera’s field of view (FOV), the camera’s focal length is adjusted automatically to compensate for the changes in the target’s image size caused by the relative motion between the camera and the target. The estimation accuracy of these changes determines the effectiveness of the resulting zoom control. The existing method of choice for real-time target scale estimation applies structure from motion (SFM) based on the weak perspective projection model. In this paper we propose a target scale estimation algorithm with a linear solution based on the more advanced paraperspective projection model, which improves the accuracy of scale estimation by considering center offset. Another key issue in SFM based algorithms is the separation of target and background features, especially when composite camera (pan/tilt/zoom) and target motions are involved. This paper designs a fast target feature separation/grouping algorithm, the 3D affine shape method. The resulting separation automatically adapts to the target’s 3D geometry and motion and is able to accommodate a large amount of off-plane rotation, which most existing separation/grouping algorithms find difficult to achieve. Experimental results illustrate the effectiveness of the proposed scale estimation and feature separation algorithms in tracking translating and rotating objects with a PTZ camera while preserving their sizes. In comparison with the leading size preserving tracking algorithm described by Tordoff and Murray, our algorithm is able to reduce the cumulative tracking error significantly from 17.4% to 3.3%.     

Mumford-Shah Regularizer with Contextual Feedback

We present a simple and robust feature preserving image regularization by letting local region measures modulate the diffusivity. The purpose of this modulation is to disambiguate low level cues in early vision. We interpret the Ambrosio-Tortorelli approximation of the Mumford-Shah model as a system with modulatory feedback and utilize this interpretation to integrate high level information into the regularization process. The method does not require any prior model or learning; the high level information is extracted from local regions and fed back to the regularization step. An important characteristic of the method is that both negative and positive feedback can be simultaneously used without creating oscillations. Experiments performed with both gray and color natural images demonstrate the potential of the method under difficult noise types, non-uniform contrast, existence of multi-scale patterns and textures.

基于有监督直接局部保持投影的人脸识别

提出一种用于图像识别的有监督直接局部保持投影算法,该算法结合样本类别信息,通过同时对角化的方法求解局部保持投影问题,避免矩阵的奇异性。在ORL人脸库上的测试结果表明,该算法的识别率高于PCA, PCA+LPP等方法。     

对加密电子医疗记录有效的连接关键词的搜索

随着云计算的发展,医院或医疗组织为了节省存储资源将加密的电子医疗记录的存储和管理外包给云服务器.尽管加密有助于保护用户数据的机密性,但是对加密的数据执行安全而有效的搜索是一个挑战性的问题.在这篇论文中,我们首先构造了被称为MCKS_I的简单的多域连接关键词搜索（MCKS）方案,该方案仅支持连接相等查询.为了实现更加灵活而复杂的多域关键词连接查询,例如子集查询和范围查询,我们又提出了被称为MCKS_II的提高方案.该方案利用了分层属性的矢量表示方法.这两个方案被证明能抵抗已知明文攻击.大量的分析和实验数据证明我们的方案是很实用的.     

有监督的无参数核局部保持投影及人脸识别

针对发掘人脸图像中的高维非线性结构,将加核及构造无参数近邻图两种思想同时引入到局部保持投影算法中,在有监督的模式下,提出了一种新的有监督的无参数核局部保持投影(Parameter-less Supervised Kernel Locality Preserving Projection,PSKLPP)算法并给出了其推导过程。该算法通过将欧氏距离改为对离群数据更为鲁棒的余弦距离,构造无参数近邻图,利用核方法提取人脸图像中的非线性信息,并将其投影在一个高维非线性空间,运用局部保持投影算法得到一线性映射,有效避免了在计算相似矩阵过程中面临的复杂参数选择问题。在ORL和Yale人脸库上的仿真实验验证了所提算法的有效性。     

基于改进的自适应局部保持投影算法的人脸识别

局部保持投影(LPP)通过构造近邻图来保持样本的局部结构,在构造近邻图的过程中,LPP会遇到两个参数K和σ的选择问题。近邻图的构建对算法的识别效果起着重要的作用,因而这两个参数的选择会在很大程度上影响LPP的识别率。为了避免参数的选择对识别率造成影响,提出了一种基于改进的自适应局部保持投影的人脸识别算法。首先,构造无参数的近邻图,其能够自适应地选取样本的近邻点并确定其相应的边权。其次,由于在计算过程中出现了矩阵维数过高的问题,因此采用QR分解进行降维处理。最后,利用共轭正交化使得投影轴具有统计不相关性,以降低特征矢量间的统计相关性,提高识别率。在ORL人脸库和YALE人脸库上进行了实验,结果表明改进的算法在识别率方面整体上好于LPP算法、DLPP算法、LMMC算法。     

On splitting-based numerical methods for nonlinear models of European options

We present a large class of nonlinear models of European options as parabolic equations with quasi-linear diffusion and fully nonlinear hyperbolic part. The main idea of the operator splitting method (OSM) is to couple known difference schemes for nonlinear hyperbolic equations with other ones for quasi-linear parabolic equations. We use flux limiter techniques, explicit–implicit difference schemes, Richardson extrapolation, etc. Theoretical analysis for illiquid market model is given. The numerical experiments show second-order accuracy for the numerical solution (the price) and Greeks Delta and Gamma, positivity and monotonicity preserving properties of the approximations.     

一种融合Hough变换和ASM定位瞳孔中心点方法

特征点的准确定位是机器视觉和模式识别的关键技术之一.主动形状模型(ASM)是一种传统的图像特征点定位算法,具有较高的精确性和鲁棒性.为了提高ASM人脸瞳孔特征点定位的精确度,提出了使用Hough变换方法来改进瞳孔特征点的定位.通过ASM算法,初步定位出瞳孔特征点,并使用Sobel算子对图像进行边缘检测,然后在人眼位置选择一个合适的窗口使用Hough圆检测,找出精确瞳孔点相对于ASM初步定位瞳孔点的偏移量.在实验室采集的人脸图像上的对比实验表明,该方法能够显著的改善ASM人眼瞳孔特征点定位准确性.由于使用了初定位进行了搜索范围的限制,计算量也得到了有效的控制.