Some Thoughts on the Emergence of Digital Close Range Photogrammetry

This presentation offers some thoughts on the emergence (that is essentially to say the commercial acceptance and exploitation) of close range digital photogrammetric systems. The discussion is restricted to systems based on standard photogrammetric processes involving digital frame imagery. Systems are classified into three tiers which are distinguished not only by accuracy and cost, but also by the degree to which a technology transfer has taken place from the specialist photogrammetrist to the non-specialist user. This technology transfer, it will be argued, has facilitated dramatic growth in the application of low accuracy, low cost three dimensional modelling systems in areas such as heritage recording, multimedia, process plant documentation and forensic photogrammetry. Along with automation, this technology transfer has also largely accounted for the wider use of high accuracy, high cost, off line and real time vision metrology systems in large scale industrial metrology. Rapid growth in these two system categories is then contrasted against the more modest advances being witnessed in the traditional close range photogrammetric service sector. Prospects for the future are also touched upon.     

GIS中空间信息多种地图显示模式的研究

通过对目前GIS中电子地图显示的分析研究,指出了GIS中空间信息地图显示存在的问题.针对目前电子地图显示模式的单一性、显示环境的多变性和显示效果的平淡性,提出了电子地图显示模式的概念.指出了影响地图显示模式变换的因素主要有空间数据的用途、屏幕比例尺、使用环境和显示的设备等,并分析了常用地图显示模式的可视化特点.对基于多种地图显示模式的符号库和显示模板进行了初步的研究,为深入研究空间信息可视化进行了初步尝试.     

Street-Frontage-Net: urban image classification using deep convolutional neural networks

ABSTRACT

Quantifying aspects of urban design on a massive scale is crucial to help develop a deeper understanding of urban designs elements that contribute to the success of a public space. In this study, we further develop the Street-Frontage-Net (SFN), a convolutional neural network (CNN) that can successfully evaluate the quality of street frontage as either being active (frontage containing windows and doors) or blank (frontage containing walls, fences and garages). Small-scale studies have indicated that the more active the frontage, the livelier and safer a street feels. However, collecting the city-level data necessary to evaluate street frontage quality is costly. The SFN model uses a deep CNN to classify the frontage of a street. This study expands on the previous research via five experiments. We find robust results in classifying frontage quality for an out-of-sample test set that achieves an accuracy of up to 92.0%. We also find active frontages in a neighbourhood has a significant link with increased house prices. Lastly, we find that active frontage is associated with more scenicness compared to blank frontage. While further research is needed, the results indicate the great potential for using deep learning methods in geographic information extraction and urban design.     

Influence of volume/mass on grain-size curves and conversion of image-analysis size to sieve size

Image analysis of aggregates does not measure the same size as sieve analysis. The size of aggregates, determined by sieve analysis, is presented with respect to the percent cumulative mass, whereas image analysis does not measure mass. Results are often presented in percent particles or percent area. Several researchers have claimed that more accurate volume and mass determinations are necessary for accurate construction of grain-size curves. In the present work, several methods for reconstructing volume and thus mass of aggregates from image analysis (IA) have been tested to see how they influence the grain-size distribution curves. The actual mass of the individual particles was found to have little or no influence on the grain-size distribution curve, which is normalized and thus insensitive to mass. Accurate conversion of image-analysis size to sieve size is dependent upon how particles pass through sieves. Most existing methods base their conversion of image-analysis size to sieve size on the intermediate axis, multiplied by some factor. The present work shows that there is no direct correlation between the intermediate axes and sieve size. A universal conversion of image-analysis size to sieve size has been developed, using the minimum-bounding square around the minimum projected area. This measure yields very good correlation with sieve-analysis results.     

中国大学生的国际观调查分析

在经济全球化背景下,中国与其他国家在经济社会等方面的交流越来越多,并深刻影响了中国公众的国际观,但中国大学生对世界的看法尚缺乏系统研究。本文对北京、上海、南京、武汉和广州等5座城市的大学生进行抽样调查以了解其国际观,通过相关分析及绘制感知地图等方法,发现中国大学生有较强烈的国家归属感,对外部世界的偏好存在着较明显的空间特征,多数大学生对欧洲版图的划分比较正确。研究结果认为中国大学生国际观的形成主要受报纸网络电视等媒体的宣传和教科书等因素的影响。     

应用机器视觉技术研究刀具磨损与耐用度

本研究采用光电摄像器件(TV摄像机或CCD)显微镜与IBM-PC计算构成的机器视觉系统,使用图像处理与模式识别技术分析和处理切削刀具后刀面上磨损带的显微影像信息;分别采用联机和脱机的方法检测、计算刀具的磨损量和损坏形态,并以此作为监测制造过程的依据,以刀具耐用度应最高、生产成本应最低为约束条件,优化了切削参数(切削速度V,进给量f)。经实验验证,理论分析计算与实测结果相吻合,为高精度识别和控制制造过程提供了一种实用方法。     

基于计算机视觉的头足类角质颚特征研究Ⅱ:形态学参数测量

角质颚形态被广泛应用于头足类种类鉴定与种群判别,基于游标卡尺的手动径向测量是获取角质颚形态参数最常用的方法.本文提出一种利用计算机视觉提取角质颚形态参数的方法,首先通过MATLAB编程提取角质颚特征点及空间坐标,然后计算特征点间的空间距离,最后将提取的角质颚形态学参数值与手动径向测量的结果进行比较.研究结果表明:利用两...     

基于线结构光视觉方法的绝缘子防污参数测量

提出了基于结构光视觉量测原理的绝缘子防污参数测量方法,采用非接触的测量方式高效地获取绝缘子带有结构光条影像,充分利用摄影测量、视觉量测技术和目标旋转体的几何结构特性,实现了变电站输变电设备带电工作状态下的绝缘子几何模型重建和防污参数量测。试验证明,该方法的自动化程度和精度可以满足绝缘子防污参数工程测量的要求,为变电设备调爬、带电水冲洗等防污工作的开展提供高精度的数据服务。     

仿生偏振特征感知与导航信息融合的空间态势感知系统

偏振成像技术已经成为有效提升空间遥感信息应用能力的有力工具。通过模拟自然界的昆虫、鸟类及鱼类偏振视觉系统的信息感知与高精度导航机制,探索基于仿生偏振视觉环境信息感知与位置姿态测量中的科学问题。构建基于偏振成像目标特征与导航信息融合的仿生态势感知系统,建立了仿生信息感知与导航解算模型,提出基于生物偏振视觉的仿生信息感知与导航的信息融合与误差分析关键算法,设计实现一种空间环境特征感知及导航信息融合的态势感知系统原理样机,数据更新率高于25 Hz,角度测量重复精度优于0.05°。     

Probabilistic forecasting of hydrological events using geostatistical analysis / Prévision probabiliste d'événements hydrologiques par analyse géostatistique

Abstract

A method is introduced for probabilistic forecasting of hydrological events based on geostatistical analysis. In this method, the predictors of a hydrological variable define a virtual field such that, in this field, the observed dependent variables are considered as measurement points. Variography of the measurement points enables the use of the system of kriging equations to estimate the value of the variable at non-measured locations of the field. Non-measured points are the forecasts associated with specific predictors. Calculation of the estimation variance facilitates probabilistic analysis of the forecast variables. The method is applied to case studies of the Red River in Manitoba, Canada and Karoon River in Khoozestan, Iran. The study analyses the advantages and limitations of the proposed method in comparison with a K-nearest neighbour approach and linear and nonlinear multiple regression. The utility of the proposed method for forecasting hydrological variables with a conditional probability distribution is demonstrated.