采用运动恢复结构(structure from motion,SFM)算法进行三维人脸建模一直以来受到研究者的关注,但其对错误的匹配点比较敏感,因此,文章提出了一种融合Gabor特征的SFM算法三维人脸建模方法。该方法利用Gabor滤波器提取纹理特征,判别轮廓...采用运动恢复结构(structure from motion,SFM)算法进行三维人脸建模一直以来受到研究者的关注,但其对错误的匹配点比较敏感,因此,文章提出了一种融合Gabor特征的SFM算法三维人脸建模方法。该方法利用Gabor滤波器提取纹理特征,判别轮廓特征点匹配的准确性;针对图像数增多,传统因子分解法不易修正旋转矩阵的问题,利用旋转矩阵的性质求得修正矩阵,避开方程组的求解;提出引入迭代最近点算法将稀疏三维特征点与三维模型进行配准,缩小空间距离,并结合薄板样条函数插值生成特定的三维人脸模型,为增强真实感,进行纹理映射。实验结果表明,该方法有效提高了匹配点的准确性,能够重建出具有较强真实感的三维人脸。展开更多
高精度、高分辨率的地形地貌数据是活动构造定量研究的基础。摄影测量方法的出现和快速发展为获取高精度地形地貌数据提供了一种经济有效的技术手段。相比于传统的测量方法,摄影测量方法可在大范围内同时进行,不受地面通视条件的限制,...高精度、高分辨率的地形地貌数据是活动构造定量研究的基础。摄影测量方法的出现和快速发展为获取高精度地形地貌数据提供了一种经济有效的技术手段。相比于传统的测量方法,摄影测量方法可在大范围内同时进行,不受地面通视条件的限制,且测量成本相对较低。尤其近年来,随着计算机视觉理论及高效的自动特征匹配算法的发展,一种名为"Structure from Motion"(SfM)的三维重建技术被引入摄影测量方法中,极大地提高了摄影测量的自动化程度。文中介绍了摄影测量方法的基本原理及发展历程,并综述了摄影测量方法在活动构造研究中的应用,最后通过SfM摄影测量方法在活动构造研究中的1个具体应用实例,展示了摄影测量方法在活动构造定量研究中的巨大应用潜力。展开更多
This paper presents a deep reinforcement learning(DRL)-based motion control method to provide unmanned aerial vehicles(UAVs)with additional flexibility while flying across dynamic unknown environments autonomously.Thi...This paper presents a deep reinforcement learning(DRL)-based motion control method to provide unmanned aerial vehicles(UAVs)with additional flexibility while flying across dynamic unknown environments autonomously.This method is applicable in both military and civilian fields such as penetration and rescue.The autonomous motion control problem is addressed through motion planning,action interpretation,trajectory tracking,and vehicle movement within the DRL framework.Novel DRL algorithms are presented by combining two difference-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem.An improved Lyapunov guidance vector field(LGVF)method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV.In contrast to conventional motion-control approaches,the proposed methods directly map the sensorbased detections and measurements into control signals for the inner loop of the UAV,i.e.,an end-to-end control.The training experiment results show that the novel DRL algorithms provide more than a 20%performance improvement over the state-ofthe-art DRL algorithms.The testing experiment results demonstrate that the controller based on the novel DRL and LGVF,which is only trained once in a static environment,enables the UAV to fly autonomously in various dynamic unknown environments.Thus,the proposed technique provides strong flexibility for the controller.展开更多
文摘采用运动恢复结构(structure from motion,SFM)算法进行三维人脸建模一直以来受到研究者的关注,但其对错误的匹配点比较敏感,因此,文章提出了一种融合Gabor特征的SFM算法三维人脸建模方法。该方法利用Gabor滤波器提取纹理特征,判别轮廓特征点匹配的准确性;针对图像数增多,传统因子分解法不易修正旋转矩阵的问题,利用旋转矩阵的性质求得修正矩阵,避开方程组的求解;提出引入迭代最近点算法将稀疏三维特征点与三维模型进行配准,缩小空间距离,并结合薄板样条函数插值生成特定的三维人脸模型,为增强真实感,进行纹理映射。实验结果表明,该方法有效提高了匹配点的准确性,能够重建出具有较强真实感的三维人脸。
文摘高精度、高分辨率的地形地貌数据是活动构造定量研究的基础。摄影测量方法的出现和快速发展为获取高精度地形地貌数据提供了一种经济有效的技术手段。相比于传统的测量方法,摄影测量方法可在大范围内同时进行,不受地面通视条件的限制,且测量成本相对较低。尤其近年来,随着计算机视觉理论及高效的自动特征匹配算法的发展,一种名为"Structure from Motion"(SfM)的三维重建技术被引入摄影测量方法中,极大地提高了摄影测量的自动化程度。文中介绍了摄影测量方法的基本原理及发展历程,并综述了摄影测量方法在活动构造研究中的应用,最后通过SfM摄影测量方法在活动构造研究中的1个具体应用实例,展示了摄影测量方法在活动构造定量研究中的巨大应用潜力。
基金supported by the National Natural Science Foundation of China(62003267)the Natural Science Foundation of Shaanxi Province(2020JQ-220)the Open Project of Science and Technology on Electronic Information Control Laboratory(JS20201100339)。
文摘This paper presents a deep reinforcement learning(DRL)-based motion control method to provide unmanned aerial vehicles(UAVs)with additional flexibility while flying across dynamic unknown environments autonomously.This method is applicable in both military and civilian fields such as penetration and rescue.The autonomous motion control problem is addressed through motion planning,action interpretation,trajectory tracking,and vehicle movement within the DRL framework.Novel DRL algorithms are presented by combining two difference-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem.An improved Lyapunov guidance vector field(LGVF)method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV.In contrast to conventional motion-control approaches,the proposed methods directly map the sensorbased detections and measurements into control signals for the inner loop of the UAV,i.e.,an end-to-end control.The training experiment results show that the novel DRL algorithms provide more than a 20%performance improvement over the state-ofthe-art DRL algorithms.The testing experiment results demonstrate that the controller based on the novel DRL and LGVF,which is only trained once in a static environment,enables the UAV to fly autonomously in various dynamic unknown environments.Thus,the proposed technique provides strong flexibility for the controller.
