In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Befo...In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.展开更多
In this work we consider the problem of shape reconstruction from an unorganized data set which has many important applications in medical imaging, scientific computing, reverse engineering and geometric modelling. Th...In this work we consider the problem of shape reconstruction from an unorganized data set which has many important applications in medical imaging, scientific computing, reverse engineering and geometric modelling. The reconstructed surface is obtained by continuously deforming an initial surface following the Partial Differential Equation (PDE)-based diffusion model derived by a minimal volume-like variational formulation. The evolution is driven both by the distance from the data set and by the curvature analytically computed by it. The distance function is computed by implicit local interpolants defined in terms of radial basis functions. Space discretization of the PDE model is obtained by finite co-volume schemes and semi-implicit approach is used in time/scale. The use of a level set method for the numerical computation of the surface reconstruction allows us to handle complex geometry and even changing topology,without the need of user-interaction. Numerical examples demonstrate the ability of the proposed method to produce high quality reconstructions. Moreover, we show the effectiveness of the new approach to solve hole filling problems and Boolean operations between different data sets.展开更多
Techniques for constructing metamodels of device parameters at BSIM3v3 level accuracy are presented to improve knowledge-based circuit sizing optimization. Based on the analysis of the prediction error of analytical p...Techniques for constructing metamodels of device parameters at BSIM3v3 level accuracy are presented to improve knowledge-based circuit sizing optimization. Based on the analysis of the prediction error of analytical performance expressions, operating point driven （OPD） metamodels of MOSFETs are introduced to capture the circuit＇s characteristics precisely. In the algorithm of metamodel construction, radial basis functions are adopted to interpolate the scattered multivariate data obtained from a well tailored data sampling scheme designed for MOSFETs. The OPD metamodels can be used to automatically bias the circuit at a specific DC operating point. Analytical-based performance expressions composed by the OPD metamodels show obvious improvement for most small-signal performances compared with simulation-based models. Both operating-point variables and transistor dimensions can be optimized in our nesting-loop optimization formulation to maximize design flexibility. The method is successfully applied to a low-voltage low-power amplifier.展开更多
基金Project supported by the National Natural Science Foundation of China (Nos. 60533090, 60525108)the National Basic Research Program (973) of China (No. 2002CB312101)+1 种基金the Science and Technology Project of Zhejiang Province, China (Nos. 2005C13032, 2005C11001-05)China-US Million Book Digital Library Project
文摘In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.
基金supported by PRIN-MIUR-Cofin 2006,project,by"Progetti Strategici EF2006"University of Bologna,and by University of Bologna"Funds for selected research topics"
文摘In this work we consider the problem of shape reconstruction from an unorganized data set which has many important applications in medical imaging, scientific computing, reverse engineering and geometric modelling. The reconstructed surface is obtained by continuously deforming an initial surface following the Partial Differential Equation (PDE)-based diffusion model derived by a minimal volume-like variational formulation. The evolution is driven both by the distance from the data set and by the curvature analytically computed by it. The distance function is computed by implicit local interpolants defined in terms of radial basis functions. Space discretization of the PDE model is obtained by finite co-volume schemes and semi-implicit approach is used in time/scale. The use of a level set method for the numerical computation of the surface reconstruction allows us to handle complex geometry and even changing topology,without the need of user-interaction. Numerical examples demonstrate the ability of the proposed method to produce high quality reconstructions. Moreover, we show the effectiveness of the new approach to solve hole filling problems and Boolean operations between different data sets.
文摘Techniques for constructing metamodels of device parameters at BSIM3v3 level accuracy are presented to improve knowledge-based circuit sizing optimization. Based on the analysis of the prediction error of analytical performance expressions, operating point driven （OPD） metamodels of MOSFETs are introduced to capture the circuit＇s characteristics precisely. In the algorithm of metamodel construction, radial basis functions are adopted to interpolate the scattered multivariate data obtained from a well tailored data sampling scheme designed for MOSFETs. The OPD metamodels can be used to automatically bias the circuit at a specific DC operating point. Analytical-based performance expressions composed by the OPD metamodels show obvious improvement for most small-signal performances compared with simulation-based models. Both operating-point variables and transistor dimensions can be optimized in our nesting-loop optimization formulation to maximize design flexibility. The method is successfully applied to a low-voltage low-power amplifier.
