Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.     

A neural network-based approach for dynamic quality prediction in a plastic injection molding process

This paper presents an innovative neural network-based quality prediction system for a plastic injection molding process. A self-organizing map plus a back-propagation neural network (SOM-BPNN) model is proposed for creating a dynamic quality predictor. Three SOM-based dynamic extraction parameters with six manufacturing process parameters and one level of product quality were dedicated to training and testing the proposed system. In addition, Taguchi’s parameter design method was also applied to enhance the neural network performance. For comparison, an additional back-propagation neural network (BPNN) model was constructed for which six process parameters were used for training and testing. The training and testing data for the two models respectively consisted of 120 and 40 samples. Experimental results showed that such a SOM-BPNN-based model can accurately predict the product quality (weight) and can likely be used for various practical applications.     

一种新的非线性分形压缩算法

提出一种基于Julia-CK集和Logistic映射的非线性分形压缩算法。用Carotid-Kundalini函数生成Julia-CK集,并用Logistic映射生成伪随机数填充量化表。将量化后的Julia-CK集分割成4 4的小图像块,再变换成圆盘。圆盘经过旋转后重新变换为正方形,对Julia-CK集进行适当的分类。编码时在同类中寻找匹配的图像块,扩充了原有的仿射变换,得到一个丰富且可通用的压缩字典,有效地打破图像和数据字典之间的一一对应关系。实验表明,相比于Barnsley提出的经典分形压缩方法,新算法使压缩比提高约36%,重建图像的峰值信噪比提高约27%,具有良好的压缩比,获得了高质量的解码图像。     

虚拟三维场景与二维电子地图交互系统研究

提出并建立了一个虚拟三维场景与二维电子地图交互系统。介绍了系统的组成和各部分实现的功能。对虚拟场景的数据组织、场景数据读入、观察者位置的确定及交互功能的实现等给出解决方案,给出了实现的代码及画面。该系统利用二维电子地图简洁性、宏观性的特点,通过与虚拟三维场景间的互响应减少观察者在虚拟三维场景中漫游所产生的迷失感,在实际应用中取得了较好的效果。     

游戏程序设计中若干问题的探讨

游戏编程是当今程序设计的一个热点,开发过程会遇到各种各样的问题。通过限制构造函数和拷贝函数,设计一个单体全局管理类,可以避免不合适的调用。采用分块技术加栽地图,可以在时空性能上优化游戏。阐述了D3D的空间坐标变换。分析了万向节死锁问题发生的原因,并提出了解决办法。     

Authorware课件网上发布解决方案研究

随着多媒体技术及其相关技术的发展,多媒体制作得到了广泛的重视,应用范围越来越广。现今网络技术的飞速发展及其广泛的应用,Authorware针对网络用户提供了一系列多媒体信息编辑、传输、网上发布的解决方案,为CAI教学在网上实现提供了良好的条件。     

改进自组织映射神经网络在指纹识别中的应用

改进自组织映射神经网络方法是将常规自组织映射神经网络方法结合确定性水平,对网络的输入矢量进行预处理。通过实验比较了这种改进的自组织映射神经网络识别方法与常规的自组织映射神经网络识别方法的识别效果,在识别性能上有了很大的提高。     

基于复合混沌动力系统的图像加密算法设计

本文通过对部分数字化混沌图像加密算法的分析研究,设计了一个基于复合离散混沌动力系统的对称图像加密算法。该对称图像加密算法充分利用混沌变化的特性,以Logistic映射和Chebyshev映射生成的混沌序列为初始序列,结合对图像的切割、拉伸、折叠变换,充分打乱图像像素间的相关性,使得针对这两个混沌映射的现有分析方法不再有效,并且可以实现较高的加密速度。实验结果证明了算法的有效性和计算速度。     

基于激光测距的环境地图动态创建技术研究

本文主要研究完全未知结构化环境下的移动机器人二维地图构建与标图技术。本文以激光测距仪为环境探测传感器,采用几何特征法创建地图。对局部地图创建中的区域分割方法进行了改进,提出了基于线性阈值法的区域分割方法;给出了基于相关线段和线段缓冲区的全局地图创建方法。实验结果表明：本方法实现了基于实时的激光测距数据的局部地图动态创建和全局地图的实时更新,算法有效且可行。     

彩色扫描地图中线目标的矢量化方法

地图要素的矢量化是从扫描地图中获取地理信息的重要问题之一.通过分析彩色扫描地图的颜色特征,提出了一种线目标半自动矢量化的方法.该方法通过在用户选择的线目标上加一个滑动窗,采用颜色空间转换、K-均值聚类和区域生长相结合的方法对窗口内的当前线目标进行自适应分割;再通过不断地沿前进方向移动窗口和对窗口内的线目标进行分割、细化及序贯跟踪,来完成线目标的矢量化.实验结果表明,文中方法快速、准确、适应性强,对于低对比度、低信噪比的彩色扫描地图尤为有效.