激光雷达扫描数据的快速三角剖分及局部优化

为了研究维激光雷达测量所得点云数据的三角构网,根据激光雷达逐行扫描特点,采用了改进的三角剖分方法,对点云数据进行不规则三角网格划分.基于激光雷达点云数据位置拓扑信息,分析了相邻扫描线之间数据点的相对位置关系,利用几何关系进行初步配对构网;并结合经典法则对初始网格进行局部优化,得到最终三角网;同时,对优化前后的三角网,提出一种新的评价法则进行剖分效果对比.结果表明,充分利用点云特点进行三角剖分可改进算法.所提出的剖分效果评价法可帮助检验构网质量.     

面聚类网格简化新算法

三维物体表面重建广泛采用三角网格方法,密集的数据采样可以重建出精确的三维表面,但是庞大的数据量不利于多分辨率三维实时显示和三维物体网络传输,因此三维表面网格简化是迫切需要解决的问题之一.近年来表面简化问题得到了广泛地研究.本文提出基于面聚类的网格简化新算法,通过最小化最大类内距离算法进行面聚类实现区域划分,然后提取区域特征,进而根据特征点和边对区域进行受限三角剖分.实验说明本文提出的面聚类网格简化算法在保持三维表面几何拓扑特征的基础上取得了很好的简化效果.     

基于多策略的三角网格面快速重构算法

针对三维散乱点云模型,设计了一种基于多策略的三角网格面快速重构算法.该算法首先利用自适应策略寻找自适应k邻,将其进行投影后在局部区域利用相交不可见策略建立顶点连接关系,在此基础上利用角度阈值策略和Delaunay准则变换策略对顶点关系进行优化,最后将此顶点关系逆映射到三维空间,得到三维点之间的连接关系,从而达到三角网格面快速重构的目的.实验结果表明此算法简单高效,处理速度快,重构效果好,并且对点云数据均匀性要求相对不高.     

截面数据重构三角网格面算法的研究

截面数据是逆向工程中常见的曲面重构数据来源。采用相邻轮廓线间添加剖分线段的方法实现三角网格面的生成,并且在传统算法基础上,加入了“缓剖分”和“错位修正”等策略,使所述算法在输出结果的准确性方面得到了大幅提升。     

利用光流估计三维运动参数的线性算法

三维空间中物体运动参数可以用二维平面光流及图象平面上投影坐标求得。当物体表面结构小于物体与投影图象平面之间的距离时,算法是线性的。     

基于射线管分裂方法的SAR 场景快速消隐技术

SAR 场景模型常采用非均匀三角网格描述,使得传统的基于Z-Buffer 技术的消隐算法难以在保持较高的消隐精度的同时兼顾消隐效率。该文提出了一种基于射线管分裂方法的SAR 场景快速消隐技术,将复杂SAR 场景的消隐问题分解为两个简单过程:一是对场景三角网格在发射平面上的投影点云做2 维Delaunay 三角网格划分,二是基于射线管分裂方法对新生网格可见性进行判断和拓扑重构。典型飞机目标和草地上T-72 坦克的消隐结果验证了该方法的准确性和高效性。      

圆柱形物体红外辐射温度场的简化算法研究

为了解决圆柱形物体的红外辐射温度场问题,对二维空间中的圆柱温度场进行了研究.首先通过建立数学模型对其进行简化和整理,然后通过对二维求解域(圆域)进行网格剖分,提出了将二维问题简化为一维问题的方法.在此网格中,先用有限差分法使微分方程离散,得出关于温度的方程组;接着分析边界条件,建立边界微分方程,并使用迭代法进行数值解算.最后,利用边界条件和初值条件求解线性方程组,得出相应结果.实验表明,该方法得出的结果与实际相符.     

基于多分辨率格网的三维物体识别方法

本文首先提出了一种改进的三维物体表达方法,它将一个三维物体表面网格与其它表面网格的几何关系表示为一个二维矩阵,称为距离角度图.这种表达能够描述任意形态物体,抑制杂乱背景和遮挡,几何意义直观,且适应不同分辨率、非规则的三角格网.然后,以这种表达方法为基础,本文阐述了一种基于多分辨率格网的,由粗到精的三维物体识别方法.它先在场景和模型的低分辨率格网上进行粗匹配以得到模型候选集合,之后在已匹配网格的高分辨率格网邻域上筛选模型候选集合,最后综合考虑多个网格对应的模型候选以得到最终模型候选的确认和验证.这种识别方法具有运算量小,准确可靠等优点,实验证明该方法正确有效.     

点云数据提取二次曲面特征算法研究

采用三维激光扫描技术可以直接得到真实物体表面的空间采样点,即点云数据,利用点云数据即可以重构三维物体表面。对重构的物体进行修复,并获得相关物理参数,是目前逆向工程重点研究问题。文章就点云数据的一些处理算法进行研究,根据点云数据的特征,利用曲面边的法向量夹角几何特征建立点云数据分割模型对点云数据分割。提取出点云数据的几何特征。对于重构的物体采用k—邻域法建立降噪模型,德洛内三角剖分法和多项式样条插值法建立三维曲面修复模型。并算法应用于具体数据中,取得良好的效果。     

基于切片-线扫描的共形网格剖分技术研究

提出了一种满足三维共形网格时域有限差分法求解的电磁目标自动几何建模方法,可实现任意材料目标组合的共形网格建模,具备较强的通用性.该方法以三角面元文件为输入数据,通过对原"切片-线扫描"法的调整,以及对网格相对面积的修正,可快速确定各网格棱边和面上的等效实体信息,实现模型的高效共形网格重构.将该方法应用于一Vivaldi天线的几何建模,其数值模拟结果与测试结果相吻合,验证了该共形网格自动剖分方法的正确性.     

Two-Dimensional Discrete Complex Image Method (DCIM) for Closed-Form Green's Function of Arbitrary 3D Structures in General Multilayered Media

The traditional discrete complex image method (DCIM) is not efficient when the source and field points are in different layers because all the spatial coordinates can not be analytically included in the image terms in spatial domain. A two dimensional method (2D-DCIM) is introduced in this paper. In the new methodology we reorganize the spectral kernel as a 2D function. We use the 2D matrix pencil method (2D-MPM) to fit this 2D function and to generate a set of complex images independent of any spatial coordinates. The closed-form spatial domain Green's function can be obtained for arbitrary locations of source and field points in general multilayered media. Compared with traditional 1D-DCIM, we do not have to run MPM for each vertical combination of source and field points. The efficiency of the matrix filling kernel of method of moments (MoM) is significantly improved.     

Robust frontal view search using extended manifold learning

Many 2D face processing algorithms can perform better using frontal or near frontal faces. In this paper, we present a robust frontal view search method based on manifold learning, with the assumption that with the pose being the only variable, face images should lie in a smooth and low-dimensional manifold. In 2D embedding, we find that manifold geometry of face images with varying poses has the shape of a parabola with the frontal view in the vertex. However, background clutter and illumination variations make frontal view deviate from the vertex. To address this problem, we propose a pairwise K-nearest neighbor protocol to extend manifold learning. In addition, we present an illumination-robust localized edge orientation histogram to represent face image in the extended manifold learning. The experimental results show that the extended algorithms have higher search accuracy, even under varying illuminations.     

基于区域聚合的栅格地形数据简化算法

针对规则网格地形数据存在的数据冗余,提出了一种基于区域聚合的地形数据简化算法,区域聚合是将共面或近似共面的若干地形栅格点用这组栅格点的边界点代替。该算法用面元间最大法向量夹角余弦值作为简化度量误差,通过递归过程完成数据简化,结合简化后存留散列点的特点进行地形绘制且经过消除地形裂缝处理,避免了离散点Delaunay三角化过程。实验结果表明,算法数据结构简单,便于使用,简化精度可控,简化效果好。     

离散系统一维不稳定流形的数值计算方法

针对二维离散系统的一维不稳定流形,本文提出一种新的算法,基本思想就是按照不稳定流形上的点的像和原像的增长比例来增长流形。同时,对该算法进行了精确的误差分析,并将该算法应用到Shear映射的不稳定流形计算中。该算法也可用于计算二维不稳定流形上的轨道,计算速度快,精度高。     

Geodesic entropic graphs for dimension and entropy estimation in manifold learning

In the manifold learning problem, one seeks to discover a smooth low dimensional surface, i.e., a manifold embedded in a higher dimensional linear vector space, based on a set of measured sample points on the surface. In this paper, we consider the closely related problem of estimating the manifold's intrinsic dimension and the intrinsic entropy of the sample points. Specifically, we view the sample points as realizations of an unknown multivariate density supported on an unknown smooth manifold. We introduce a novel geometric approach based on entropic graph methods. Although the theory presented applies to this general class of graphs, we focus on the geodesic-minimal-spanning-tree (GMST) to obtaining asymptotically consistent estimates of the manifold dimension and the Re/spl acute/nyi /spl alpha/-entropy of the sample density on the manifold. The GMST approach is striking in its simplicity and does not require reconstruction of the manifold or estimation of the multivariate density of the samples. The GMST method simply constructs a minimal spanning tree (MST) sequence using a geodesic edge matrix and uses the overall lengths of the MSTs to simultaneously estimate manifold dimension and entropy. We illustrate the GMST approach on standard synthetic manifolds as well as on real data sets consisting of images of faces.     

基于高斯增量降维与流形Boltzmann优化的人体运动形态估计

为了从多视角轮廓图像估计出含空间位置信息的三维人体运动形态,该文提出高斯增量降维与流形Boltzmann优化（GIDRMBO）算法.该算法把表示三维人体运动形态的高维数据分成表示空间位置信息和姿态信息两段子向量后,用高斯增量降维模型（GIDRM）分别对其样本进行降维,建立相应的低维空间及映射关系,然后在相应的低维空间使用流形Boltzmann优化算法来对轮廓匹配目标函数进行优化,从而实现估计.其中,所提算法分别利用了两段子向量样本的低维数据作为先验信息,可较好的避免陷入局部最优区域进行搜索,最终生成与各视角原始运动图像匹配且含空间位置信息的三维人体运动形态.经仿真实验验证,所提算法与常用粒子滤波算法相比,其估计误差小,并且还能起到消除轮廓数据歧义和克服短时遮挡的作用.     

Interference-aware feedback based iterative Hungarian approach to allocate multiple channels to multiple D2D pairs to maximize underlay D2D network capacity

The spectral efficiency of a cellular network can be increased significantly by allowing spatial reuse of its spectrum by an underlay device-to-device (D2D) network. In an underlay D2D network, devices in close vicinity are allowed to establish low-power direct links with little to no involvement of the base station. In order to increase the spectral efficiency and the number of devices with channel access, multiple D2D pairs may transmit in each cellular channel. Additionally, each pair can be allowed to utilize multiple channels to transmit so as to maximize the D2D network capacity. This multiple-pair multiple-channel (MPMC) strategy is quite appealing but is limited by the resultant additional aggregate interference and the inherent complexity, hence necessitating the need for a fast and reliable channel allocation scheme. This work proposes a polynomial-time iterative Hungarian assignment with feedback (IHAF) algorithm for multiple channel allocations amongst multiple D2D pairs that increases the D2D network capacity manifold while maintaining the desired minimum capacity for each cellular user.     

Local maximal margin discriminant embedding for face recognition

In this paper, a manifold learning based method named local maximal margin discriminant embedding (LMMDE) is developed for feature extraction. The proposed algorithm LMMDE and other manifold learning based approaches have a point in common that the locality is preserved. Moreover, LMMDE takes consideration of intra-class compactness and inter-class separability of samples lying in each manifold. More concretely, for each data point, it pulls its neighboring data points with the same class label towards it as near as possible, while simultaneously pushing its neighboring data points with different class labels away from it as far as possible under the constraint of locality preserving. Compared to most of the up-to-date manifold learning based methods, this trick makes contribution to pattern classification from two aspects. On the one hand, the local structure in each manifold is still kept in the embedding space; one the other hand, the discriminant information in each manifold can be explored. Experimental results on the ORL, Yale and FERET face databases show the effectiveness of the proposed method.     

LLE-based classification algorithm for MMW radar target recognition

In this paper, a new multiclass classification algorithm is proposed based on the idea of Locally Linear Embedding (LLE), to avoid the defect of traditional manifold learning algorithms, which can not deal with new sample points. The algorithm defines an error as a criterion by computing a sample’s reconstruc-tion weight using LLE. Furthermore, the existence and characteristics of low dimensional manifold in range-profile time-frequency information are explored using manifold learning algorithm, aiming at the problem of target recognition about high range resolution MilliMeter-Wave (MMW) radar. The new algo-rithm is applied to radar target recognition. The experiment results show the algorithm is efficient. Com-pared with other classification algorithms, our method improves the recognition precision and the result is not sensitive to input parameters.     

雷达信号的扩散特征及其在特定辐射源识别中的应用

本文利用Yoyos直观系统模型与随机微分几何,分析特定辐射源识别问题,为该问题建立了一种有意义的几何学描述.通过上述模型及分析,指出辐射源个体所辐射信号的瞬时参数中包含具有内蕴性质的指纹特征信息,且由产生信号的系统低维状态流形决定.扩散映射是一种新兴的流形学习算法,已有研究与实践证明该算法可以在提取高维数据蕴含的低维流形的同时较完整地保持采样点之间的几何性质.本文利用扩散映射的这一良好特性,结合所建立的直观模型,提取信号瞬时参数的扩散特征,用于特定辐射源识别,取得了较好的效果.最后通过外场实验,验证了上述模型与特征的正确性和有效性.