In this paper we present the MEMPHIS middleware framework for the integration of CAD geometries and assemblies with derived Virtual Reality (VR) models and its specific meta data and attributes. The goal of this work ...In this paper we present the MEMPHIS middleware framework for the integration of CAD geometries and assemblies with derived Virtual Reality (VR) models and its specific meta data and attributes. The goal of this work is to connect real time VR applications, especially for the Design Review, with enterprise software storing and managing CAD models (Product Data Man- agement—PDM). The preparation of VR models requires expert knowledge, is time consuming, and includes selection of required CAD data, tessellation, healing of unwanted gaps, applying materials and textures, and special surface and light effects. During the Design Review process, decisions are made concerning the choice of materials and surface forms. While materials can be switched directly on the VR model, the modification of part geometries must be made on the CAD model. Our system synchronizes modi- fications of the original CAD geometries and of attributes that are relevant for the realistic rendering using the PLM Services standard. Thus, repeated work for the VR preparation can be avoided.展开更多
Visual tracking is a challenging problem in computer vision. Recently, correlation filter-based trackers have shown to provide excellent tracking performance. Inspired by a sample consensus approach proposed for foreg...Visual tracking is a challenging problem in computer vision. Recently, correlation filter-based trackers have shown to provide excellent tracking performance. Inspired by a sample consensus approach proposed for foreground detection, which classifies a given pixel as foreground or background based on its similarity to recently observed samples, we present a template consensus tracker based on the kernelized correlation filter (KCF). Instead of keeping only one target appearance model in the KCF, we make a feature pool to keep several target appearance models in our method and predict the new target position by searching for the location of the maximal value of the response maps. Both quantitative and qualitative evaluations are performed on the CVPR2013 tracking benchmark dataset. The results show that our proposed method improves the original KCF tracker by 8.17% in the success plot and 8.11% in the precision plot.展开更多
Cooperative target identification is the prerequisite for the relative position and orientation measurement between the space robot arm and the to-be-arrested object. We propose an on- orbit real-time robust algorithm...Cooperative target identification is the prerequisite for the relative position and orientation measurement between the space robot arm and the to-be-arrested object. We propose an on- orbit real-time robust algorithm for cooperative target identification in complex background using the features of circle and lines. It first extracts only the interested edges in the target image using an adaptive threshold and refines them to about single-pixel-width with improved non-maximum suppression. Adapting a novel tracking approach, edge segments changing smoothly in tangential directions are obtained. With a small amount of calculation, large numbers of invalid edges are removed. From the few remained edges, valid circular arcs are extracted and reassembled to obtain circles according to a reliable criterion. Finally, the target is identified if there are certain numbers of straight lines whose relative positions with the circle match the known target pattern. Experiments demonstrate that the proposed algorithm accurately identifies the cooperative target within the range of 0.3 1.5 m under complex background at the speed of 8 frames per second, regardless of lighting condition and target attitude. The proposed algorithm is very suitable for real-time visual measurement of space robot arm because of its robustness and small memory requirement.展开更多
基金Project supported by the Korean Ministry of Information and Communication (MIC)
文摘In this paper we present the MEMPHIS middleware framework for the integration of CAD geometries and assemblies with derived Virtual Reality (VR) models and its specific meta data and attributes. The goal of this work is to connect real time VR applications, especially for the Design Review, with enterprise software storing and managing CAD models (Product Data Man- agement—PDM). The preparation of VR models requires expert knowledge, is time consuming, and includes selection of required CAD data, tessellation, healing of unwanted gaps, applying materials and textures, and special surface and light effects. During the Design Review process, decisions are made concerning the choice of materials and surface forms. While materials can be switched directly on the VR model, the modification of part geometries must be made on the CAD model. Our system synchronizes modi- fications of the original CAD geometries and of attributes that are relevant for the realistic rendering using the PLM Services standard. Thus, repeated work for the VR preparation can be avoided.
基金supported by the National Natural Science Foundation of China(No.61401425)
文摘Visual tracking is a challenging problem in computer vision. Recently, correlation filter-based trackers have shown to provide excellent tracking performance. Inspired by a sample consensus approach proposed for foreground detection, which classifies a given pixel as foreground or background based on its similarity to recently observed samples, we present a template consensus tracker based on the kernelized correlation filter (KCF). Instead of keeping only one target appearance model in the KCF, we make a feature pool to keep several target appearance models in our method and predict the new target position by searching for the location of the maximal value of the response maps. Both quantitative and qualitative evaluations are performed on the CVPR2013 tracking benchmark dataset. The results show that our proposed method improves the original KCF tracker by 8.17% in the success plot and 8.11% in the precision plot.
基金supported by the National Basic Research Program of China (No. 2013CB733103)
文摘Cooperative target identification is the prerequisite for the relative position and orientation measurement between the space robot arm and the to-be-arrested object. We propose an on- orbit real-time robust algorithm for cooperative target identification in complex background using the features of circle and lines. It first extracts only the interested edges in the target image using an adaptive threshold and refines them to about single-pixel-width with improved non-maximum suppression. Adapting a novel tracking approach, edge segments changing smoothly in tangential directions are obtained. With a small amount of calculation, large numbers of invalid edges are removed. From the few remained edges, valid circular arcs are extracted and reassembled to obtain circles according to a reliable criterion. Finally, the target is identified if there are certain numbers of straight lines whose relative positions with the circle match the known target pattern. Experiments demonstrate that the proposed algorithm accurately identifies the cooperative target within the range of 0.3 1.5 m under complex background at the speed of 8 frames per second, regardless of lighting condition and target attitude. The proposed algorithm is very suitable for real-time visual measurement of space robot arm because of its robustness and small memory requirement.
