In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel （two photographs） for each building. In most cases, however, only a single non-metric photograph is available, which is us...In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel （two photographs） for each building. In most cases, however, only a single non-metric photograph is available, which is usually obtained either by an amateur, such as a tourist, or from a newspaper or a post card. To evaluate the validity of 3D reconstruction from a single non-metric image, this study analyzes the effects of object depth on the accuracy of dimensional shape in X and Y directions using a single non-metric image by means of simulation technique, as this was considered to be, in most cases, a main source of data acquisition in recording and documenting buildings.展开更多
Array of Things (AoT)通过单一位置上的多传感器对城市系统进行连续动态观测。AoT观测数据量大且持续增长,使得如何利用有限的计算资源进行AoT序列数据的压缩传输成为其应用的关键瓶颈之一。本文提出了一种基于张量分解的AoT序列数据...Array of Things (AoT)通过单一位置上的多传感器对城市系统进行连续动态观测。AoT观测数据量大且持续增长,使得如何利用有限的计算资源进行AoT序列数据的压缩传输成为其应用的关键瓶颈之一。本文提出了一种基于张量分解的AoT序列数据的有损压缩方法。面向其海量、高维且需在传感器端处理的需求,该方法首先将AoT序列数据组织成高维张量,利用算法复杂度较低的张量CANDECOMP/PARAFAC (CP)分解提取各维度上的特征主分量,而后利用张量重构实现特征保持的数据有损压缩。利用基于张量分解的有损压缩方法,针对美国芝加哥市区的24 h内感测的声光电磁数据进行了实验,讨论了不同压缩参数对压缩比、压缩误差、压缩精度、压缩时间、压缩过程运行内存占用和压缩结果内存占用之间的影响。实验结果表明该方法可实现AoT序列数据的有损压缩,其较小的内存占用能够支持传感器端数据压缩。并且与原始光场强度对比表明,压缩后的数据保持了原有时空分布特征。与传统矢量量化编码压缩方法相比,在相同压缩精度下,本文方法的压缩比约高27%~76%,压缩时间约节省46%~73%,压缩结果所占内存约节省17%~57%,因此本文方法具有更高的压缩比,更低的压缩时间和内存占用,可为Ao T这一类数据的大规模有损压缩提供借鉴意义。展开更多
文摘In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel （two photographs） for each building. In most cases, however, only a single non-metric photograph is available, which is usually obtained either by an amateur, such as a tourist, or from a newspaper or a post card. To evaluate the validity of 3D reconstruction from a single non-metric image, this study analyzes the effects of object depth on the accuracy of dimensional shape in X and Y directions using a single non-metric image by means of simulation technique, as this was considered to be, in most cases, a main source of data acquisition in recording and documenting buildings.
文摘Array of Things (AoT)通过单一位置上的多传感器对城市系统进行连续动态观测。AoT观测数据量大且持续增长,使得如何利用有限的计算资源进行AoT序列数据的压缩传输成为其应用的关键瓶颈之一。本文提出了一种基于张量分解的AoT序列数据的有损压缩方法。面向其海量、高维且需在传感器端处理的需求,该方法首先将AoT序列数据组织成高维张量,利用算法复杂度较低的张量CANDECOMP/PARAFAC (CP)分解提取各维度上的特征主分量,而后利用张量重构实现特征保持的数据有损压缩。利用基于张量分解的有损压缩方法,针对美国芝加哥市区的24 h内感测的声光电磁数据进行了实验,讨论了不同压缩参数对压缩比、压缩误差、压缩精度、压缩时间、压缩过程运行内存占用和压缩结果内存占用之间的影响。实验结果表明该方法可实现AoT序列数据的有损压缩,其较小的内存占用能够支持传感器端数据压缩。并且与原始光场强度对比表明,压缩后的数据保持了原有时空分布特征。与传统矢量量化编码压缩方法相比,在相同压缩精度下,本文方法的压缩比约高27%~76%,压缩时间约节省46%~73%,压缩结果所占内存约节省17%~57%,因此本文方法具有更高的压缩比,更低的压缩时间和内存占用,可为Ao T这一类数据的大规模有损压缩提供借鉴意义。
