为了快速、准确地获取掌子面岩体结构面产状信息,提出了基于数字近景摄影测量技术的隧道掌子面岩体结构面产状的自动提取方法。通过拍摄多组隧道开挖面的图像,生成开挖隧道岩体表面的三维实体模型和点云模型,基于k-means++算法和DBSCAN(...为了快速、准确地获取掌子面岩体结构面产状信息,提出了基于数字近景摄影测量技术的隧道掌子面岩体结构面产状的自动提取方法。通过拍摄多组隧道开挖面的图像,生成开挖隧道岩体表面的三维实体模型和点云模型,基于k-means++算法和DBSCAN(density-based spatial clustering of applications with noise)算法实现了掌子面点云法向量的自动分组与结构面圆盘拟合,通过计算结构面圆盘的法向量获得了结构面的产状。将该方法应用于朱家山公路隧道开挖面结构面倾向倾角的获取,研究结果表明:该方法可快速生成开挖隧道三维点云模型,能够非接触式地识别统计掌子面岩体的结构面信息,尤其适用于岩体破碎而技术人员接触测量有安全危险的掌子面,实现了隧道围岩结构面信息的自动提取。展开更多
Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face rec...Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.展开更多
文摘为了快速、准确地获取掌子面岩体结构面产状信息,提出了基于数字近景摄影测量技术的隧道掌子面岩体结构面产状的自动提取方法。通过拍摄多组隧道开挖面的图像,生成开挖隧道岩体表面的三维实体模型和点云模型,基于k-means++算法和DBSCAN(density-based spatial clustering of applications with noise)算法实现了掌子面点云法向量的自动分组与结构面圆盘拟合,通过计算结构面圆盘的法向量获得了结构面的产状。将该方法应用于朱家山公路隧道开挖面结构面倾向倾角的获取,研究结果表明:该方法可快速生成开挖隧道三维点云模型,能够非接触式地识别统计掌子面岩体的结构面信息,尤其适用于岩体破碎而技术人员接触测量有安全危险的掌子面,实现了隧道围岩结构面信息的自动提取。
文摘Despite the fact that progress in face recognition algorithms over the last decades has been made, changing lighting conditions and different face orientation still remain as a challenging problem. A standard face recognition system identifies the person by comparing the input picture against pictures of all faces in a database and finding the best match. Usually face matching is carried out in two steps： during the first step detection of a face is done by finding exact position of it in a complex background （various lightning condition）, and in the second step face identification is performed using gathered databases. In reality detected faces can appear in different position and they can be rotated, so these disturbances reduce quality of the recognition algorithms dramatically. In this paper to increase the identification accuracy we propose original geometric normalization of the face, based on extracted facial feature position such as eyes. For the eyes localization lbllowing methods has been used： color based method, mean eye template and SVM （Support Vector Machine） technique. Experimental investigation has shown that the best results for eye center detection can be achieved using SVM technique. The recognition rate increases statistically by 28% using face orientation normalization based on the eyes position.
