非正则精确模型的布尔操作

为了加强造型系统的表达能力,实现对线框模型、曲面模型和实体模型的统一表示和操作,该文采用非正则精确模型作为几何对象表示的数据结构,并给出了相应的布尔操作实现算法,在保证效率和精确性的同时确保鲁棒性是算法的目标.作为一个特征造型系统的几何核心,该算法已经在基于NT平台的特征造型系统GS-CAD98中实现.     

A sufficient condition for a wire-frame representing a solid modeling uniquely

Generally Speaking,it is impossible for a wire-frame to define a 3D object uniquely.But wire-frame as a graplhics medium is still appled in some industrial areas.A sufficient condition is presented in this paper,If this condition is satisfied by a wire-frame,then the wire-frame can represent a 3D object uniquely.The result is applied to manufacturing of progressive stripe.     

利用主动外观模型进行3D人脸的快速自动建模

针对传统方法存在的通用性不强、实时性较差和与视频传输系统不兼容等缺点,提出一种面向单幅任意姿态图像的3D人脸建模技术.首先将基于初始位置校正和模型实例选择的主动外观模型改进方法应用到人脸特征点的提取过程中;然后结合人脸结构特征和空间仿射变换调节CANDIDE-3线框模型,实现对应人脸的全局位置恢复和形状匹配.在此基础上,根据感兴趣区域的对称性对局部坐标进行微调,并构建真实感纹理.实验结果表明,该技术不受图像拍摄焦距等因素影响,单幅图像平均建模时间约为300ms.     

Facial Motion Analysis for Content-based Video Coding

Automatic wire-frame fitting and automatic wire-frame tracking are the two most important and most difficult issues associated with semantic-based moving image coding. A novel approach to high speed tracking of important facial features is presented as a part of a complete fitting-tracking system. The method allows real-time processing ofhead-and-shoulders sequences using software tools only. The algorithm is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the video sequence. Each important facial feature (the left eye, the right eye, the nose and the lips) is tracked separately using the same method. The algorithm was tested on widely used head-and-shoulders video sequences containing the speaker's head pan, rotation and zoom with remarkably good results. These experiments prove that it is possible to maintain tracking even when the facial features are partially occluded.     

MA3D：三维造型动画系统的OOP实现

本文介绍了一个基于ＯＯＰ程序设计方法的三维造型动车集成系统，在该系统中，造型与动画具有统一的数据结构，系统提供的命令语言使得用户能够定义模型及其运动，系统输出的数据文件用以描述动画的每一帧画面，它们可以作为生成三维浓淡图的绘制程序的输入，从而生成三维动车画面。目前，系统提供了以线框图形式进行动画预演的工具。     

由物体线框模型构建表面模型研究综述

三维重建过程中线框模型往往作为中间模型出现,由于线框模型的二义性以及过 于简单的信息制约了它的应用,通常都要将其转换为表面模型或实体模型。该文主要围绕三维 重建中线框模型的表面构建问题,分析其中所面临的难点。根据三维重建源对象的不同,对直 接从线框模型提取特征构建表面模型、面向线框模型从单幅线图构建表面模型以及基于多面投 影图构建表面模型三个方面,综述了当前对线框模型进行表面构建的方法。在此基础上指出研 究中尚存在的问题并展望未来的发展趋势。     

Reconstruction of curved solids from engineering drawings

This paper presents a new approach for reconstructing solids with planar, quadric and toroidal surfaces from three-view engineering drawings. By applying geometric theory to 3-D reconstruction, our method is able to remove restrictions placed on the axes of curved surfaces by existing methods. The main feature of our algorithm is that it combines the geometric properties of conics with affine properties to recover a wider range of 3-D edges. First, the algorithm determines the type of each 3-D candidate conic edge based on its projections in three orthographic views, and then generates that candidate edge using the conjugate diameter method. This step produces a wire-frame model that contains all candidate vertices and candidate edges. Next, a maximum turning angle method is developed to find all the candidate faces in the wire-frame model. Finally, a general and efficient searching technique is proposed for finding valid solids from the candidate faces; the technique greatly reduces the searching space and the backtracking incidents. Several examples are given to demonstrate the efficiency and capability of the proposed algorithm.     

Constructing a wire-frame from views on arbitrary view planes for objects with conic sections inclined to all view planes

A new and efficient approach to construct a 3D wire-frame of an object from its orthographic projections is described. The input projections can be two or more and can include regular and complete auxiliary views. Each view may contain linear, circular and other conic sections. The output is a 3D wire-frame that is consistent with the input views.The approach can handle auxiliary views containing curved edges. This generality derives from a new technique to construct 3D vertices from the input 2D vertices (as opposed to matching coordinates that is prevalent in current art). 3D vertices are constructed by projecting the 2D vertices in a pair of views on the common line of the two views. The construction of 3D edges also does not require the addition of silhouette and tangential vertices and subsequently splitting edges in the views. The concepts of complete edges and n-tuples are introduced to obviate this need. Entities corresponding to the 3D edge in each view are first identified and the 3D edges are then constructed from the information available with the matching 2D edges. This allows the algorithm to handle conic sections that are not parallel to any of the viewing directions. The localization of effort in constructing 3D edges is the source of efficiency of the construction algorithm as it does not process all potential 3D edges.Working of the algorithm on typical drawings is illustrated.     

时态数据的一种挖掘算法

时态数据挖掘是数据挖掘中一个日益重要的研究课题。本文针对时态数据中的多维关系模型，提出了一种新的时态数据建模算法。     

A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

Almost all subspace clustering algorithms proposed so far are designed for numeric datasets. In this paper, we present a k-means type clustering algorithm that finds clusters in data subspaces in mixed numeric and categorical datasets. In this method, we compute attributes contribution to different clusters. We propose a new cost function for a k-means type algorithm. One of the advantages of this algorithm is its complexity which is linear with respect to the number of the data points. This algorithm is also useful in describing the cluster formation in terms of attributes contribution to different clusters. The algorithm is tested on various synthetic and real datasets to show its effectiveness. The clustering results are explained by using attributes weights in the clusters. The clustering results are also compared with published results.     

3D Surface Reconstruction of Noisy Point Clouds Using Growing Neural Gas: 3D Object/Scene Reconstruction

With the advent of low-cost 3D sensors and 3D printers, scene and object 3D surface reconstruction has become an important research topic in the last years. In this work, we propose an automatic (unsupervised) method for 3D surface reconstruction from raw unorganized point clouds acquired using low-cost 3D sensors. We have modified the growing neural gas network, which is a suitable model because of its flexibility, rapid adaptation and excellent quality of representation, to perform 3D surface reconstruction of different real-world objects and scenes. Some improvements have been made on the original algorithm considering colour and surface normal information of input data during the learning stage and creating complete triangular meshes instead of basic wire-frame representations. The proposed method is able to successfully create 3D faces online, whereas existing 3D reconstruction methods based on self-organizing maps required post-processing steps to close gaps and holes produced during the 3D reconstruction process. A set of quantitative and qualitative experiments were carried out to validate the proposed method. The method has been implemented and tested on real data, and has been found to be effective at reconstructing noisy point clouds obtained using low-cost 3D sensors.     

质量度量指标驱动的数据聚合与多维数据可视化

以多维数据可视化为研究对象,在质量度量模型下,采用数据聚合为基本手段,来提高多维数据可视化的图像质量.在质量度量指标驱动的框架下提出了均分 K-means++数据聚合算法,在传统 K-means算法的基础上,专门以数据可视化为目的对算法进行了改进,使得算法聚合得到的数据既能够较好地保持原数据的大部分特性,又能显著地提高可视化后的图像质量.仿真实验证明,在不同的数据抽象级别DAL下,无论是图像质量指标还是质量度量指标HDM（直方图差值度量）、NNM（最近邻距离度量）,算法都表现出了较好的仿真结果.     

大数据下关联规则算法的改进及应用

大数据时代对数据挖掘的技术和应用提出了更高的要求,关联规则算法作为数据挖掘的一个主要方向,能够在大量数据中发现频繁项集和关联知识。Apriori算法是关联规则的经典算法,本文对其在大数据下应用的缺点提出改进的方法,并结合用户收视行为的海量数据对改进后的算法进行应用,提高了数据挖掘的效率并得到较好的挖掘结果,同时为后续的应用提出了新的课题。     

基于全局优化的影象变形方法

该文首先对影象计算中变形算法的研究作了一个简要的概述，然后结合我们目前所进行的智能动画的研究工作，提出了一个在影象空间中基于全局优化的影象变形算法。在文中第二节较主详细地介绍了该算法中所用到的散乱点播值算法，最后给出了该算法的详细描述。     

Mixing numerical and categorical data in a Self-Organizing Map by means of frequency neurons

Even though Self-Organizing Maps (SOMs) constitute a powerful and essential tool for pattern recognition and data mining, the common SOM algorithm is not apt for processing categorical data, which is present in many real datasets. It is for this reason that the categorical values are commonly converted into a binary code, a solution that unfortunately distorts the network training and the posterior analysis. The present work proposes a SOM architecture that directly processes the categorical values, without the need of any previous transformation. This architecture is also capable of properly mixing numerical and categorical data, in such a manner that all the features adopt the same weight. The proposed implementation is scalable and the corresponding learning algorithm is described in detail. Finally, we demonstrate the effectiveness of the presented algorithm by applying it to several well-known datasets.     

一种基于聚类的文本迁移学习算法

当现有训练数据过期,而新数据又非常少时,运用迁移学习能够有效提高分类器性能。本文提出一种基于聚类的文本迁移学习算法,给出了算法的主要思想及实现步骤。然后,在中文文本语料库上进行了实验,并与非迁移学习算法进行了比较。实验证明该方法能有效提高分类器性能。     

Algorithm for Extracting Loosely Structured Data Records Through Digging Strict Patterns

Extracting loosely structured data records (LSDRs) has wide applications in many domains, such as forum pattern recognition, Weblogs data analysis, and books and news review analysis. Yet currently existing methods only work well for strongly structured data records (SDRs). In this paper, we propose to address the problem of extracting LSDRs through mining strict patterns. In our method, we utilize both content feature and tag tree feature to recognize the LSDRs, and propose a new algorithm to extract the Data Records (DRs) automatically. The experimental results demonstrate that our algorithm is able to effectively extract LSDRs with higher precision and recall.     

A NEW ALGORITHM FOR ESTIMATING THE RISK OF NATURAL DISASTERS WITH INCOMPLETE DATA

In this paper, we propose a new algorithm for estimating the risk of natural disaster based on incomplete data. To guarantee reliability in theory, we prove in this paper that the estimator obtained by the algorithm is asymptotically unbiased and mean squared consistent. We also give two simulation experiments showing the advantage of the algorithm. To demonstrate its practicality, we further employ the new algorithm to estimate, with only 9 observations, the risk of flood, drought and wind in Changsha county of Hunan Province in China. All results show that the new algorithm, which can unravel fuzzy information in incomplete data, is better than the main existing methods for risk estimation of natural disaster with small samples.     

数据仓库中基于密度的批量增量聚类算法

数据仓库为数据挖掘提供了很好的平台,当数据仓库中的数据发生变化时,原来挖掘出来的模式也要相应地进行更新。MartinEster等最先提出了增量聚类算法,但算法在增量聚类过程中,更新对象依次一个个地单独处理,而没有考虑更新对象之间的关系,效率较低。该文提出了基于DBSCAN算法的批量增量聚类算法,减少了对象的检索,提高了增量聚类的效率。     

时间序列数据分析与预处理

时间序列分析中常常遇到的一个问题是如何有效地过滤噪音和约简数据。本文通过修改传统的离散的傅立叶变换来过滤噪音和进行数据的约简，并尽可能保留原始时间序列的全局变化趋势。为检验该方法的有效性，本文同时提出一个新颖的数据分类算法MCC，并用该算法对股票回报率的变化进行预测，实验结果显示，用MCC算法在预处理后的数据上进行预测，其预测的命中率达到63．68％，而在原始数据上进行预测，其预测的命中率只有48．98％。显然，通过对原始数据进行噪音过滤有效地改善了预测的精度。另外，数据的约简也提高了预测算法的效率。