A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classif...A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.展开更多
高精度车内定位技术是提供车内智能服务、进行车内用户行为习惯分析等应用的基础,有重要实用价值。低功耗蓝牙(BLE, bluetooth low energy)的RSSI(received signal strength indicator)值可用于定位系统的分析计算。针对无线信号传输易...高精度车内定位技术是提供车内智能服务、进行车内用户行为习惯分析等应用的基础,有重要实用价值。低功耗蓝牙(BLE, bluetooth low energy)的RSSI(received signal strength indicator)值可用于定位系统的分析计算。针对无线信号传输易受环境影响的问题,对车内定位提出了一种基于蓝牙多信道多RSSI值(multi-channel multi-RSSI values)的车内定位方法 VehLoc。接收端在传统的采集蓝牙RSSI信号的基础上,同时记录信号的信道来源,通过使用3个蓝牙信标在其不同信道的RSSI值对使用者终端在车内的位置进行粗细粒度与分布相结合的区域分析和位置判断。实验结果表明,VehLoc定位方法对车内5个主要位置的分类正确率均可达90%。展开更多
文摘A novel and fast shape classification and regularization algorithm for on-line sketchy graphics recognition is proposed. We divide the on-line graphics recognition process into four stages: preprocessing,shape classification,shape fitting,and regularization. Attraction Force Model is employed to progressively combine the vertices on the input sketchy stroke and reduce the total number of vertices before the type of shape can be determined. After that ,the shape is fitted and gradually rectified to a regular one,thus the regularized shape fits the user intended one precisely.Experimental results show that this algorithm can yield good recognition precision(averagely above 90% )and fine regularization effect but with fast speed. Consequently,it is especially suitable to computational critical environment such as PDAs,which solely depends on a pen-based user interface.
文摘高精度车内定位技术是提供车内智能服务、进行车内用户行为习惯分析等应用的基础,有重要实用价值。低功耗蓝牙(BLE, bluetooth low energy)的RSSI(received signal strength indicator)值可用于定位系统的分析计算。针对无线信号传输易受环境影响的问题,对车内定位提出了一种基于蓝牙多信道多RSSI值(multi-channel multi-RSSI values)的车内定位方法 VehLoc。接收端在传统的采集蓝牙RSSI信号的基础上,同时记录信号的信道来源,通过使用3个蓝牙信标在其不同信道的RSSI值对使用者终端在车内的位置进行粗细粒度与分布相结合的区域分析和位置判断。实验结果表明,VehLoc定位方法对车内5个主要位置的分类正确率均可达90%。
