9910585
Video based human animation technique
Human animation is a challenging domain in computer animation. To aim at many shortcomings in conventional techniques, this paper proposes a new video based human animation technique. Given a clip of video, firstly human joints are tracked with the support of Kalman filter and morph-block based match in the image sequence. Then corresponding sequence of three-dimension (3D) human motion skeleton is constructed under the perspective projection using camera calibration and human anatomy knowledge. Finally a motion library is established automatically by annotating multiform motion attributes, which can be browsed and queried by the animator. This approach has the characteristic of rich source material, low computing cost, efficient production, and realistic animation result. We demonstrate it on several video clips of people doing full body movements, and visualize the results by re-animating a 3D human skeleton model.
8133100
Geometry-assisted statistical modeling for face mosaicing
The modeling of facial appearance has many applications. This paper proposes an approach to generating a statistical face model based on video mosaicing. Unlike traditional video mosaicing, we use the geometry of a face to improve the mosaicing result. Given a face sequence, each frame is unwrapped onto certain portion of the surface of a sphere, as determined by spherical projection and the minimization procedure using the Levenberg-Marquardt algorithm. A statistical model containing a mean image and a number of eigenimages, instead of only one image template, is used to represent the face mosaic. Good experimental results have been observed.
16757439
Odd Hole Recognition in Graphs of Bounded Clique Size
In a graph $G$, an odd hole is an induced odd cycle of length at least 5. A clique of $G$ is a set of pairwise adjacent vertices. In this paper we consider the class ${\cal C}_k$ of graphs whose cliques have a size bounded by a constant $k$. Given a graph $G$ in ${\cal C}_k$, we show how to recognize in polynomial time whether $G$ contains an odd hole.
14510898
Evolutionary learning of dynamic probabilistic models with large time lags
In this paper, we explore the automatic explanation of multivariate time series (MTS) through learning dynamic Bayesian networks (DBNs). We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare this algorithm to other standard learning algorithms that have traditionally been used for static Bayesian networks but are adapted for DBNs in this paper. These are extensively tested on both synthetic and real-world MTS for various aspects of efficiency and accuracy. By proposing a simple representation scheme, an efficient learning methodology, and several useful heuristics, we have found that the proposed method is more efficient for learning DBNs from MTS with large time lags, especially in time-demanding situations. © 2001 John Wiley & Sons, Inc.
792291
Variable grouping in multivariate time series via correlation
The decomposition of high-dimensional multivariate time series (MTS) into a number of low-dimensional MTS is a useful but challenging task because the number of possible dependencies between variables is likely to be huge. This paper is about a systematic study of the "variable groupings" problem in MTS. In particular, we investigate different methods of utilizing the information regarding correlations among MTS variables. This type of method does not appear to have been studied before. In all, 15 methods are suggested and applied to six datasets where there are identifiable mixed groupings of MTS variables. This paper describes the general methodology, reports extensive experimental results, and concludes with useful insights on the strength and weakness of this type of grouping method.
6185811
An efficient large deformation method using domain decomposition
Efficiently simulating large deformations of flexible objects is a challenging problem in computer graphics. In this paper, we present a physically based approach to this problem, using the linear elasticity model and a finite elements method. To handle large deformations in the linear elasticity model, we exploit the domain decomposition method, based on the observation that each sub-domain undergoes a relatively small local deformation, involving a global rigid transformation. In order to efficiently solve the deformation at each simulation time step, we pre-compute the object responses in terms of displacement accelerations to the forces acting on each node, yielding a force-displacement matrix. However, the force-displacement matrix could be too large to handle for densely tessellated objects. To address this problem, we present two methods. The first method exploits spatial coherence to compress the force-displacement matrix using the clustered principal component analysis method; and the second method pre-computes only the force-displacement vectors for the boundary vertices of the sub-domains and resorts to the Cholesky factorization to solve the acceleration for the internal vertices of the sub-domains. Finally, we present some experimental results to show the large deformation effects and fast performance on complex large scale objects under interactive user manipulations.
426496
Freeze: engineering a fast repeater insertion solver for power minimization using the ellipsoid method
This paper presents a novel repeater insertion algorithm for the power minimization of realistic interconnect trees under given timing budgets. Our algorithm judiciously combines a local optimizer based on the dynamic programming technique and a global search engine using the ellipsoid method. As a result, our approach is capable of producing high-quality solutions at a very fast speed. Furthermore, our scheme is robust and does not need any manual tuning of the iteration-control parameters. We have developed a repeater insertion tool, called Freeze, using the proposed algorithm and applied it to various interconnect trees with different timing targets. Experimental results demonstrate the high effectiveness of our approach. In comparison with the state-of-the-art low-power repeater insertion schemes, Freeze requires 5.8 times fewer iterations on the average, achieving up to 27 times speedup with even better power savings. When compared with a dynamic programming based scheme, which guarantees the optimal solution, our tool delivers up to 50 times speedup with 0.9% power increase on the average.