A Hierarchical 3-D Poisson Modified Fourier Solver by Domain Decomposition

We present a Domain Decomposition non-iterative solver for the Poisson equation in a 3-D rectangular box. The solution domain is divided into mostly parallelepiped subdomains. In each subdomain a particular solution of the non-homogeneous equation is first computed by a fast spectral method. This method is based on the application of the discrete Fourier transform accompanied by a subtraction technique. For high accuracy the subdomain boundary conditions must be compatible with the specified inhomogeneous right hand side at the edges of all the interfaces. In the following steps the partial solutions are hierarchically matched. At each step pairs of adjacent subdomains are merged into larger units. In this paper we present the matching algorithm for two boxes which is a basis of the domain decomposition scheme. The hierarchical approach is convenient for parallelization and minimizes the global communication. The algorithm requires O(N ³:log:N) operations, where N is the number of grid points in each direction.     

基于骨架层次分解的目标的图表示

基于骨架的目标表示技术是模式识别和计算机视觉的重要研究内容,近年来人们提出了许多骨架化算法,但是有关利用骨架信息表示并识别目标的研究还非常有限。Ablameyko等1996年提出了通过分解由距离标号的骨架为有意义的结构基元从而获得目标的层次结构图的方法。该图可以准确地刻画基元之间的拓扑关系,但是它对于骨架中的噪声比较敏感。主要表现为噪声基元破坏其它基元的完整性和图的稳定性。该文采用将分支编组为分支链以及构造多尺度结构图的改进策略来克服这些缺点,最终获得了目标的节点数更小、节点显著度更高、节点间连接关系更稳定的多尺度图,从而显著地提高后续利用不精确图匹配技术进行目标识别的效率。这项技术已经被应用于一个基于形状特征的图像数据库检索系统中。     

一种对二维空间对象进行聚类的算法

地理信息系统存储了大量的二维空间对象,对这些对象进行聚类分析是数据挖掘的一项重要任务.本文提出一种针对二维空间对象的聚类算法.该算法引用层次聚类方法的思想,将子聚类信息用一个聚类特征表示.采用基于密度的方法,发现任意形状的簇,能较好地处理孤立点,并且支持增量式聚类.实验证明该算法是有效的.     

三维空间对象拓扑关系的研究

在数字地球、数字城市、地理信息系统等涉及地理数据等许多应用中,三维空间对象的拓扑关系的研究是一个十分重要的问题;弄清三维空间对象的拓扑关系,对于三维空间对象的操作以及操作的算法研究至关重要。由于三维空间对象的复杂性,它们之间拓扑关系的判定大多只是从简单的概念来进行判断,还没有从理论上全面地给定它的判定准则。该文试图在三维空间中,详细地研究空间对象之间拓扑关系成立的条件与结论,利用这些规则,可以全面地得到三维空间对象之间可能存在的所有拓扑关系。     

A Model for Representing Programs Using Hierarchical Graphs

In this paper a hierarchical graph model for programs based on the concepts of recursive graphs (RG's) and Codd relations is presented. The purpose of the model is to clearly represent the structure of a program implemented in a structured language, such as Pascal, Algol, or PL/1, so that the program can be analyzed and modifications to the program can be clearly specified. The model uses an RG representation for the control flow and the data flow with an equivalent relational representation. It also has a relational representation for the objects defmed within the program. The various aspects of the model are illustrated using Pascal constructs and a model for an example Pascal program is given.     

用代数系统描述数据对象及其操作方法的研究

本文旨在研究使用代数系统对CMM管理工具所需数据对象及其上操作进行统一描述的方法,结合代数系统的定义和定理,将数据对象的全体看作集合,将数据对象上的各种操作定义成该集合上的运算,从而构成代数系统,实例证明,本方法可以和已有的抽象代数系统联系起来,从而为控制数据对象上的各种操作提供可靠的理论依据。     

面向数据流的多层Count-Min概要数据结构

构造了多层Count-Min概要数据结构来概括流数据中的层次结构。通过定义多层数据域U*上两两相互独立的异或哈希函数族,将数据流元组映射到L×D×W的三维计数数组,L是层次个数,D是从哈希函数族中均匀随机选取的哈希函数个数,W是哈希函数的值域。基于该结构,利用广度优先查询策略,查找多层频繁项集和估计多层频繁项值。实验表明,该结构在更新时间、存储空间和估计精度方面比直接堆叠多个Count-Min结构有较大的提高。     

A Spatial Access-Oriented Implementation of a 3-D GIS Topological Data Model for Urban Entities

3-D analysis in GIS is still one of the most challenging topics for research. With the goal being to model possible movement within the built environment, this paper, therefore, proposes a new approach to handling connectivity relationships among 3-D objects in urban environments in order to implement spatial access analyses in 3-D space. To achieve this goal, this paper introduces a 3-D network data model called the geometric network model (GNM), which has been developed by transforming the combinatorial data model (CDM), representing a connectivity relationship among 3-D objects using a dual graph. For the transformation, this paper presents (1) an O(n ²) algorithm for computing a straight medial axis transformation (MAT), (2) the processes for transforming phenomena from 3-D CDM to 3-D GNM, and (3) spatial access algorithms for the 3-D geometric network based upon the Dijkstra algorithm. Using the reconstructed geometric network generated from the transformations, spatial queries based upon the complex connectivity relationships between 3-D urban entities are implemented using Dijkstra algorithm. Finally, the paper presents the results of an experimental implementation of a 3-D network data model (GNM) using GIS data of an area in downtown Columbus, Ohio.     

A Hierarchical RBF Online Learning Algorithm for Real-Time 3-D Scanner

In this paper, a novel real-time online network model is presented. It is derived from the hierarchical radial basis function (HRBF) model and it grows by automatically adding units at smaller scales, where the surface details are located, while data points are being collected. Real-time operation is achieved by exploiting the quasi-local nature of the Gaussian units: through the definition of a quad-tree structure to support their receptive field local network reconfiguration can be obtained. The model has been applied to 3-D scanning, where an updated real-time display of the manifold to the operator is fundamental to drive the acquisition procedure itself. Quantitative results are reported, which show that the accuracy achieved is comparable to that of two batch approaches: batch HRBF and support vector machines (SVMs). However, these two approaches are not suitable to real-time online learning. Moreover, proof of convergence is also given.      

A Constraint-Satisfaction Approach for 3-D Object Recognition by Integrating 2-D and 3-D Data

We present a framework for recognition of 3D objects by integrating 2D and 3D sensory data. The major thrust of this work is to efficiently utilize all relevant data, both 2D and 3D, in the early stages of recognition, in order to reduce the computational requirements of the recognition process. To achieve this goal, we formulate the problem as a constraint–satisfaction problem (CSP). Rather than directly solving the CSP, a problem of exponential complexity, we only enforce local consistency in low-order polynomial time. This step of local-consistency enforcement can significantly decrease the computational load on subsequent recognition modules by (1) significantly reducing the uncertainty in the correspondence between scene and model features and (2) eliminating many erroneous model objects and irrelevant scene features. A novel method is presented for efficiently constructing a CSP corresponding to a combination of 2D and 3D scene features. Performance of the proposed framework is demonstrated using simulated and real experiments involving visual (2D) and tactile (3D) data.     

The OR-Tree:一种用于空间对象序列查询的动态索引

几乎所有的多维空间索引都没有考虑空间对象之间的顺序关系,只支持单个空间对象的索引和查询,无法直接支持空间对象序列查询.本文在R-Tree的基础上,提出一种可直接用于空间对象序列查询的动态索引--OR-Tree,保存了空间对象序列中对象之间的序关系.时间序列的相似性查询实验表明:与R-Tree相比,基于OR-Tree的方法在磁盘I/O次数和查询结果的候选集大小上显著降低,并且查询序列越长,性能提高就越明显.     

Computation of Independent Contact Regions for Grasping 3-D Objects

Precision grasp synthesis has received a lot of attention in past few last years. However, real mechanical hands can hardly assure that the fingers will precisely touch the object at the computed contact points. The concept of independent contact regions (ICRs) was introduced to provide robustness to finger positioning errors during an object grasping: A finger contact anywhere inside each of these regions assures a force-closure grasp, despite the exact contact position. This paper presents an efficient algorithm to compute ICRs with any number of frictionless or frictional contacts on the surface of any 3-D object. The proposed approach generates the independent regions by growing them around the contact points of a given starting grasp. A two-phase approach is provided to find a locally optimal force-closure grasp that serves as the starting grasp, considering as grasp quality measure the largest perturbation wrench that the grasp can resist, independently of the perturbation direction. The proposed method can also be applied to compute ICRs when several contacts are fixed beforehand. The approach has been implemented, and application examples are included to illustrate its performance.      

The DR-tree: A Main Memory Data Structure for Complex Multi-dimensional Objects

An efficient index structure for complex multi-dimensional objects is one of the most challenging requirements in non-traditional applications such as geographic information systems, computer-aided design, and multimedia databases. In this paper we first propose a main memory data structure for complex multi-dimensional objects. Then, we present an extension of the existing multi-dimensional index structure. Among existing multi-dimensional index structures, the popular R*-tree is selected. The R*-tree is coupled with the main memory data structure to improve the performance of spatial query processing. An analytical model is developed for our index structure. Experimental results show that the analytical model is accurate, the relative error being below 15%. The performance of our index structure is compared with that of a state-of-the-art index structure by experimental measurements. Our index structure outperforms the state-of-the-art index structure due to its ability to reduce a large amount of storage.     

典型地物的光谱空间结构特征研究

地物光谱是遥感技术应用与研究的物理基础。本文提出将地物在光谱特征空间的几何点阵结构特征作为研究重点,着重分析植被、土壤、岩石的野外实测光谱数据的光谱空间结构特征,并对所测得地物的光谱数据进行光谱线性混合模拟,进而分析线性混合地物的光谱空间结构特征。对实测地物光谱数据空间结构特征的分析不仅可以指导遥感影像数据的分析与应用,而且也为遥感影像地物目标识别和矿化蚀变信息提取技术的研究提供更多的科学理论依据和新的研究思路。     

An Automated Finite Polygon Division Method for 3-D Objects

The automatic generation of color-shading data for CAD/CAM objects can produce mass-property calculations in less time with fewer errors.     

Probabilistic Modeling and Recognition of 3-D Objects

This paper introduces a uniform statistical framework for both 3-D and 2-D object recognition using intensity images as input data. The theoretical part provides a mathematical tool for stochastic modeling. The algorithmic part introduces methods for automatic model generation, localization, and recognition of objects. 2-D images are used for learning the statistical appearance of 3-D objects; both the depth information and the matching between image and model features are missing for model generation. The implied incomplete data estimation problem is solved by the Expectation Maximization algorithm. This leads to a novel class of algorithms for automatic model generation from projections. The estimation of pose parameters corresponds to a non-linear maximum likelihood estimation problem which is solved by a global optimization procedure. Classification is done by the Bayesian decision rule. This work includes the experimental evaluation of the various facets of the presented approach. An empirical evaluation of learning algorithms and the comparison of different pose estimation algorithms show the feasibility of the proposed probabilistic framework.     

一种基于层次栈的XML数据小枝查询算法研究

当前针对小枝模式的XML查询是XML文档查询的研究热点。文章在分析XML数据小枝查询处理常用算法的基础上,提出了一种高灵活性的、易确定结点对之间结构关系的EDiezt-P编码,并基于EDiezt-P编码和层次栈结构提出了一种自底向上的小枝查询算法。实验表明,该算法在一定程度上减少了查询处理时间,提高了查询效率。