Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However...Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition （HHD） based object-motion oriented map （OOM）. By decomposing the image motion （optical flow） into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%-20% even over challenging scenes with complex background.展开更多
为提升弹载成像制导中运动模糊图像目标检测的精确性与效率,提出一种轻量化且高效的运动模糊图像目标检测(Lighter and More Effective Motion-blurred Image Object Detection,LEMBD)网络。通过深入分析运动模糊图像的成因,基于成像机...为提升弹载成像制导中运动模糊图像目标检测的精确性与效率,提出一种轻量化且高效的运动模糊图像目标检测(Lighter and More Effective Motion-blurred Image Object Detection,LEMBD)网络。通过深入分析运动模糊图像的成因,基于成像机理构建了专用的运动模糊图像数据集。在不增加网络参数的前提下,采用共享权重的孪生网络设计,并引入先验知识,将清晰图像的特征学习用于模糊图像的特征提取,以同时实现对清晰与模糊图像的精准检测。此外,设计了部分深度可分离卷积替代普通卷积,显著减少了网络的参数量与计算量,并提升了学习性能。为进一步优化特征融合质量,提出跨层路径聚合特征金字塔网络,有效利用低级特征的细节信息和高级特征的语义信息。实验结果表明,所提LEMBD网络在运动模糊图像目标检测任务中的性能优于传统目标检测方法和主流运动模糊检测算法,能够为精确制导任务提供更精准的目标相对位置信息。展开更多
柔顺机构在输入输出方向容易产生非期望方向的寄生运动,不利于机构的驱动和运动控制。为解决此问题,该文提出了一种考虑寄生运动的柔顺机构拓扑优化方法。基于固体各向同性材料惩罚模型(Solid isotropic material with penalization,SI...柔顺机构在输入输出方向容易产生非期望方向的寄生运动,不利于机构的驱动和运动控制。为解决此问题,该文提出了一种考虑寄生运动的柔顺机构拓扑优化方法。基于固体各向同性材料惩罚模型(Solid isotropic material with penalization,SIMP)方法,将寄生运动引入目标函数,其中旋转运动使用两点的平动位移差值进行表示。考虑寄生运动和输出位移的性能关系,结合权重因子将多目标转换成单目标,建立了柔顺机构的拓扑优化模型。采用Heaviside函数进行密度过滤,并利用优化准则法(Optimality criteria,OC)进行求解。以输入端低寄生运动的柔顺放大机构设计为例,给出了3种不同情况下的拓扑优化结果。并利用ANSYS Workbench对优化机构进行有限元仿真分析,验证了该方法的有效性。展开更多
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61503277.
文摘Motion segmentation in moving camera videos is a very challenging task because of the motion dependence between the camera and moving objects. Camera motion compensation is recognized as an effective approach. However, existing work depends on prior-knowledge on the camera motion and scene structure for model selection. This is not always available in practice. Moreover, the image plane motion suffers from depth variations, which leads to depth-dependent motion segmentation in 3D scenes. To solve these problems, this paper develops a prior-free dependent motion segmentation algorithm by introducing a modified Helmholtz-Hodge decomposition （HHD） based object-motion oriented map （OOM）. By decomposing the image motion （optical flow） into a curl-free and a divergence-free component, all kinds of camera-induced image motions can be represented by these two components in an invariant way. HHD identifies the camera-induced image motion as one segment irrespective of depth variations with the help of OOM. To segment object motions from the scene, we deploy a novel spatio-temporal constrained quadtree labeling. Extensive experimental results on benchmarks demonstrate that our method improves the performance of the state-of-the-art by 10%-20% even over challenging scenes with complex background.
文摘为提升弹载成像制导中运动模糊图像目标检测的精确性与效率,提出一种轻量化且高效的运动模糊图像目标检测(Lighter and More Effective Motion-blurred Image Object Detection,LEMBD)网络。通过深入分析运动模糊图像的成因,基于成像机理构建了专用的运动模糊图像数据集。在不增加网络参数的前提下,采用共享权重的孪生网络设计,并引入先验知识,将清晰图像的特征学习用于模糊图像的特征提取,以同时实现对清晰与模糊图像的精准检测。此外,设计了部分深度可分离卷积替代普通卷积,显著减少了网络的参数量与计算量,并提升了学习性能。为进一步优化特征融合质量,提出跨层路径聚合特征金字塔网络,有效利用低级特征的细节信息和高级特征的语义信息。实验结果表明,所提LEMBD网络在运动模糊图像目标检测任务中的性能优于传统目标检测方法和主流运动模糊检测算法,能够为精确制导任务提供更精准的目标相对位置信息。
文摘柔顺机构在输入输出方向容易产生非期望方向的寄生运动,不利于机构的驱动和运动控制。为解决此问题,该文提出了一种考虑寄生运动的柔顺机构拓扑优化方法。基于固体各向同性材料惩罚模型(Solid isotropic material with penalization,SIMP)方法,将寄生运动引入目标函数,其中旋转运动使用两点的平动位移差值进行表示。考虑寄生运动和输出位移的性能关系,结合权重因子将多目标转换成单目标,建立了柔顺机构的拓扑优化模型。采用Heaviside函数进行密度过滤,并利用优化准则法(Optimality criteria,OC)进行求解。以输入端低寄生运动的柔顺放大机构设计为例,给出了3种不同情况下的拓扑优化结果。并利用ANSYS Workbench对优化机构进行有限元仿真分析,验证了该方法的有效性。
