Image mosaicking is widely used in Geographic Information Systems(GISs)for largescale ground surface analysis.However,most existing mosaicking methods can only be used in offline processing due to the enormous amounts...Image mosaicking is widely used in Geographic Information Systems(GISs)for largescale ground surface analysis.However,most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation.In this paper,we propose a novel and practical algorithm for real-time infrared video mosaicking.To achieve this,a novel fast template matching algorithm based on Sum of Cosine Differences(SCD)is proposed to coarsely match the sequential images.The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform(FFT).We also propose a novel fast Least Squares Matching(LSM)algorithm for inter-frame fine registration,which can significantly reduce the computation without degrading the matching accuracy.In addition,the proposed fast LSM can effectively adapt for noise degradation and geometric distortion.Based on the proposed fast template matching algorithm and fine registration algorithm,we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently.Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.展开更多
Building a post-layout simulation performance model is essential in closing the loop of analog circuits, but it is a challenging task because of the high-dimensional space and expensive simulation cost. To facilitate ...Building a post-layout simulation performance model is essential in closing the loop of analog circuits, but it is a challenging task because of the high-dimensional space and expensive simulation cost. To facilitate efficient modeling, this paper proposes a Global Mapping Model Fusion(GMMF) technique. The key idea of GMMF is to reuse the schematic-level model trained by the Artificial Neural Network(ANN) algorithm, and combine it with few mapping coefficients to build the post-simulation model. Furthermore, as an efficient global optimization algorithm,differential evolution is applied to determine the optimal mapping coefficients with few samples. In GMMF, only a small number of mapping coefficients are unknown, so the number of post-layout samples needed is significantly reduced. To enhance practical utility of the proposed GMMF technique, two specific mapping relations, i.e., linear or weakly no-linear and nonlinear, are carefully considered in this paper. We conduct experiments on two topologies of two-stage operational amplifier and comparator in different commercial processes. All the simulation data for modeling are obtained from a parametric design framework. A more than 5 runtime speedup is achieved over ANN without surrendering any accuracy.展开更多
High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on ...High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost.Hence,this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data.The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles.This allows users to modify the extraction process by using a more sophisticated neural network,thus achieving a more accurate detection result when compared with traditional binarization method.The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks.Finally,the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition.展开更多
An Augmented virtual environment(AVE)is concerned with the fusion of real-time video with 3D models or scenes so as to augment the virtual environment.In this paper,a new approach to establish an AVE with a wide field...An Augmented virtual environment(AVE)is concerned with the fusion of real-time video with 3D models or scenes so as to augment the virtual environment.In this paper,a new approach to establish an AVE with a wide field of view is proposed,including real-time video projection,multiple video texture fusion and 3D visualization of moving objects.A new diagonally weighted algorithm is proposed to smooth the apparent gaps within the overlapping area between the two adjacent videos.A visualization method for the location and trajectory of a moving virtual object is proposed to display the moving object and its trajectory in the 3D virtual environment.The experimental results showed that the proposed set of algorithms are able to fuse multiple real-time videos with 3D models efficiently,and the experiment runs a 3D scene containing two million triangles and six real-time videos at around 55 frames per second on a laptop with 1GB of graphics card memory.In addition,a realistic AVE with a wide field of view was created based on the Digital Earth Science Platform by fusing three videos with a complex indoor virtual scene,visualizing a moving object and drawing its trajectory in the real time.展开更多
基金supported by the National Natural Science Foundation of China(No.61802423)the Natural Science Foundation of Hunan Province,China(No.2019JJ50739)。
文摘Image mosaicking is widely used in Geographic Information Systems(GISs)for largescale ground surface analysis.However,most existing mosaicking methods can only be used in offline processing due to the enormous amounts of computation.In this paper,we propose a novel and practical algorithm for real-time infrared video mosaicking.To achieve this,a novel fast template matching algorithm based on Sum of Cosine Differences(SCD)is proposed to coarsely match the sequential images.The high speed of the proposed template matching algorithm is obtained by computing correlation with Fast Fourier Transform(FFT).We also propose a novel fast Least Squares Matching(LSM)algorithm for inter-frame fine registration,which can significantly reduce the computation without degrading the matching accuracy.In addition,the proposed fast LSM can effectively adapt for noise degradation and geometric distortion.Based on the proposed fast template matching algorithm and fine registration algorithm,we develop a practical real-time mosaicking approach which can produce seamless mosaic image highly efficiently.Experiments on synthetic and real-world datasets demonstrate that the proposed algorithm is not just computationally efficient but also robust against various noise distortions.
基金supported by the National Key Technology Research and Development Program (Nos.2018YFB2202701 and 2019YFB2205003)the National Major Research Program from Ministry of Science and Technology of China (No. 2016YFA0201903)Science and Technology Program from Beijing Science and Technology Commission (No. Z201100004220003)。
文摘Building a post-layout simulation performance model is essential in closing the loop of analog circuits, but it is a challenging task because of the high-dimensional space and expensive simulation cost. To facilitate efficient modeling, this paper proposes a Global Mapping Model Fusion(GMMF) technique. The key idea of GMMF is to reuse the schematic-level model trained by the Artificial Neural Network(ANN) algorithm, and combine it with few mapping coefficients to build the post-simulation model. Furthermore, as an efficient global optimization algorithm,differential evolution is applied to determine the optimal mapping coefficients with few samples. In GMMF, only a small number of mapping coefficients are unknown, so the number of post-layout samples needed is significantly reduced. To enhance practical utility of the proposed GMMF technique, two specific mapping relations, i.e., linear or weakly no-linear and nonlinear, are carefully considered in this paper. We conduct experiments on two topologies of two-stage operational amplifier and comparator in different commercial processes. All the simulation data for modeling are obtained from a parametric design framework. A more than 5 runtime speedup is achieved over ANN without surrendering any accuracy.
基金This work was supported in part by National Natural Science Foundation of China(U186420361773234 and 52102464)Project Funded by China Postdoctoral Science Foundation(2019M660622)in part by the International Science and Technology Cooperation Program of China(2019YFE0100200).
文摘High-definition map has become a vital cornerstone in the navigation of autonomous vehicles in complex traffic scenarios.Thus,the construction of high-definition maps has become crucial.Traditional methods relying on expensive mapping vehicles equipped with high-end sensor equipment are not suitable for mass map construction because of the limitation imposed by its high cost.Hence,this paper proposes a new method to create a high-definition road semantics map using multi-vehicle sensor data.The proposed method implements crowdsourced point-based visual SLAM to align and combine the local maps derived by multiple vehicles.This allows users to modify the extraction process by using a more sophisticated neural network,thus achieving a more accurate detection result when compared with traditional binarization method.The resulting map consists of road marking points suitable for autonomous vehicle navigation and path-planning tasks.Finally,the method is evaluated on the real-world KAIST urban dataset and Shougang dataset to demonstrate the level of detail and accuracy of the proposed map with 0.369 m in mapping errors in ideal condition.
基金Research presented in this paper was funded by the National Key Research and Development Program of China[grant numbers 2016YFB0501503 and 2016YFB0501502]Hainan Provincial Department of Science and Technology[grant number ZDKJ2016021].
文摘An Augmented virtual environment(AVE)is concerned with the fusion of real-time video with 3D models or scenes so as to augment the virtual environment.In this paper,a new approach to establish an AVE with a wide field of view is proposed,including real-time video projection,multiple video texture fusion and 3D visualization of moving objects.A new diagonally weighted algorithm is proposed to smooth the apparent gaps within the overlapping area between the two adjacent videos.A visualization method for the location and trajectory of a moving virtual object is proposed to display the moving object and its trajectory in the 3D virtual environment.The experimental results showed that the proposed set of algorithms are able to fuse multiple real-time videos with 3D models efficiently,and the experiment runs a 3D scene containing two million triangles and six real-time videos at around 55 frames per second on a laptop with 1GB of graphics card memory.In addition,a realistic AVE with a wide field of view was created based on the Digital Earth Science Platform by fusing three videos with a complex indoor virtual scene,visualizing a moving object and drawing its trajectory in the real time.
