During the new product development process, reusing the existing CAD models could avoid designing from scratch and decrease human cost. With the advent of big data,how to rapidly and efficiently find out suitable 3D C...During the new product development process, reusing the existing CAD models could avoid designing from scratch and decrease human cost. With the advent of big data,how to rapidly and efficiently find out suitable 3D CAD models for design reuse is taken more attention. Currently the sketch-based retrieval approach makes search more convenient, but its accuracy is not high enough; on the other hand, the semantic-based retrieval approach fully utilizes high level semantic information, and makes search much closer to engineers＇ intent.However, effectively extracting and representing semantic information from data sets is difficult.Aiming at these problems, we proposed a sketch-based semantic retrieval approach for reusing3 D CAD models. Firstly a fine granularity semantic descriptor is designed for representing 3D CAD models; Secondly, several heuristic rules are adopted to recognize 3D features from 2D sketch, and the correspondences between 3D feature and 2D loops are built; Finally, semantic and shape similarity measurements are combined together to match the input sketch to 3D CAD models. Hence the retrieval accuracy is improved. A sketch-based prototype system is developed.Experimental results validate the feasibility and effectiveness of our proposed approach.展开更多
Image registration is an old topic but has a new application in deep-sky imaging fields named live stacking. In this Letter, we propose a live stacking algorithm based on star detection, description, and matching. A t...Image registration is an old topic but has a new application in deep-sky imaging fields named live stacking. In this Letter, we propose a live stacking algorithm based on star detection, description, and matching. A thresholding method based on Otsu and centralization is proposed to implement star detection. Then, a translation and rotation invariant descriptor is proposed to provide accurate feature matching. Extensive experiments illustrate that our proposed method is feasible in deep-sky image live stacking.展开更多
基金Supported by the National Natural Science Foundation of China(61502129,61572432,61163016)the Zhejiang Natural Science Foundation of China(LQ16F020004,LQ15F020011)the University Scientific Research Projects of Ningxia Province of China(NGY2015161)
文摘During the new product development process, reusing the existing CAD models could avoid designing from scratch and decrease human cost. With the advent of big data,how to rapidly and efficiently find out suitable 3D CAD models for design reuse is taken more attention. Currently the sketch-based retrieval approach makes search more convenient, but its accuracy is not high enough; on the other hand, the semantic-based retrieval approach fully utilizes high level semantic information, and makes search much closer to engineers＇ intent.However, effectively extracting and representing semantic information from data sets is difficult.Aiming at these problems, we proposed a sketch-based semantic retrieval approach for reusing3 D CAD models. Firstly a fine granularity semantic descriptor is designed for representing 3D CAD models; Secondly, several heuristic rules are adopted to recognize 3D features from 2D sketch, and the correspondences between 3D feature and 2D loops are built; Finally, semantic and shape similarity measurements are combined together to match the input sketch to 3D CAD models. Hence the retrieval accuracy is improved. A sketch-based prototype system is developed.Experimental results validate the feasibility and effectiveness of our proposed approach.
文摘Image registration is an old topic but has a new application in deep-sky imaging fields named live stacking. In this Letter, we propose a live stacking algorithm based on star detection, description, and matching. A thresholding method based on Otsu and centralization is proposed to implement star detection. Then, a translation and rotation invariant descriptor is proposed to provide accurate feature matching. Extensive experiments illustrate that our proposed method is feasible in deep-sky image live stacking.
