Three-dimensional animated face modeling from stereo video
3D modeling of human face has been an active research topic in computer vision and computer graphics communities. In this paper we develop a face-modeling system from calibrated stereo video input. The stereo configuration is chosen according to the characteristics of the face structure. For data capturing the subject sits in front of the calibrated stereo video cameras and rotates his/her head from front view to side view. Then we can create his/her 3D face model with realistic texture mapped, which is ready for MPEG-4 compatible facial animation, by adapting a generic wireframe model to the input. In deciding 3D face geometry we utilize two kinds of 2D information. One is the correspondence between stereo pair; the other is self-occluded contour. After some 3D points in the specific face are obtained from both image clues in different frames, RBF (Radius Basis Function) data interpolation is performed to decide its shape. Afterwards texture image is generated from the face geometry and video. Experiments show that satisfactory results can be reached with our system.

title: VISATRAM: a real-time vision system for automatic traffic monitoring
abstract: This paper presents a novel approach to automatic traffic monitoring using 2D spatio-temporal images. A TV camera is mounted above a highway to monitor the traffic through two slice windows, and a panoramic view image and an epipolar plane image are formed for each lane. Our real-time vision system for automatic traffic monitoring, VISATRAM, is an inexpensive system with a PC486 and a frame grabber. The system cannot only count vehicles and estimate their speeds but also classify them using 3D measurements. The system has been tested with real road images under various light conditions, including shadows in daytime and lights at night.

title: Learning-Based Tracking of Complex Non-Rigid Motion
abstract: This paper describes a novel method for tracking complex non-rigid motions by learning the intrinsic object structure.The approach builds on and extends the studies on non-linear dimensionality reduction for object representation,object dynamics modeling and particle filter style tracking.First,the dimensionality reduction and density estimation algorithm is derived for unsupervised learning of object intrinsic representation,and the obtained non-rigid part of object state reduces even to 2-3 dimensions.Secondly the dynamical model is derived and trained based on this intrinsic representation.Thirdly the learned intrinsic object structure is integrated into a particle filter style tracker.It is shown that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle filter style tracker more robust and reliable.Extensive experiments are done on the tracking of challenging non-rigid motions such as fish twisting with selfocclusion,large inter-frame lip motion and facial expressions with global head rotation.Quantitative results are given to make comparisons between the newly proposed tracker and the existing tracker.The proposed method also has the potential to solve other type of tracking problems.

title: Efficient Fourier-Based Approach for Detecting Orientations and Occlusions in Epipolar Plane Images for 3D Scene Modeling
abstract: This paper presents a Fourier-based approach for automatically constructing a 3D panoramic model of a natural scene from a video sequence. The video sequences could be captured by an unstabilized camera mounted on a moving platform on a common road surface. As the input of the algorithms, “seamles” panoramic view images (PVIs) and epipolar plane images (EPIs) are generated after image stabilization if the camera is unstabilized. A novel panoramic EPI analysis method is proposed that combines the advantages of both PVIs and EPIs efficiently in three important steps: locus orientation detection in the Fourier frequency domain, motion boundary localization in the spatio-temporal domain, and occlusion/resolution recovery only at motion boundaries. The Fourier energy-based approaches in literature were usually for low-level local motion analysis and are therefore not accurate for 3D reconstruction and are also computationally expensive. Our panoramic EPI analysis approach is both accurate and efficient for 3D reconstruction. Examples of layered panoramic representations for large-scale 3D scenes from real world video sequences are given.

title: Human action recognition with primitive-based coupled-HMM
abstract: This paper presents a new approach named primitive-based coupled-HMM for human natural complex action recognition. First, the system proposes a hybrid human model and employs 2-order B-spline function to detect the two shoulder joints in the silhouette image to obtain the basic motion features including the elbow angles, motion parameters of the face and two hands. Then, primitive-based coupled hidden Markov model (PCHMM) is presented for natural context-dependent action recognition. Lastly, comparison experiments show that PCHMM is better than the conventional HMM and coupled HMM.

title: 3D model based expression tracking in intrinsic expression space
abstract: A method of learning the intrinsic facial expression space for expression tracking is proposed. First, a partial 3D face model is constructed from a trinocular image and the expression space is parameterized using MPEG4 FAP. Then an algorithm of learning the intrinsic expression space from the parameterized FAP space is derived. The resulted intrinsic expression space reduces even to 5 dimensions. We will show that the obtained expression space is superior to the space obtained by PCA. Then the dynamical model is derived and trained on this intrinsic expression space. Finally, the learned tracker is developed in a particle-filter-style tracking framework. Experiments on both synthetic and real videos show that the learned tracker performs stably over a long sequence and the results are encouraging.

title: Minimal Mean-Curvature-Variation Surfaces and Their Applications in Surface Modeling
abstract: Physical based and geometric based variational techniques for surface construction have been shown to be advanced methods for designing high quality surfaces in the fields of CAD and CAGD. In this paper, we derive a Euler-Lagrange equation from a geometric invariant curvature integral functional–the integral about the mean curvature gradient. Using this Euler-Lagrange equation, we construct a sixth-order geometric flow (named as minimal mean-curvature-variation flow), which is solved numerically by a divided-difference-like method. We apply our equation to solving several surface modeling problems, including surface blending, N-sided hole filling and point interpolating. The illustrative examples provided show that this sixth-order flow yields high quality surfaces.

title: Automatic reconstruction of surfaces and scalar fields from 3D scans
abstract: We present an efficient and uniform approach for the automatic reconstruction of surfaces of CAD (computer aided design) models and scalar fields defined on them, from an unorganized collection of scanned point data. A possible application is the rapid computer model reconstruction of an existing part or prototype from a three dimensional (3D) points scan of its surface. Color, texture or some scalar material property of the physical part, define natural scalar fields over the surface of the CAD model. Our reconstruction algorithm does not impose any convexity or differentiability restrictions on the surface of the original physical part or the scalar field function, except that it assumes that there is a sufficient sampling of the input point data to unambiguously reconstruct the CAD model. Compared to earlier methods our algorithm has the advantages of simplicity, efficiency and uniformity (both CAD model and scalar field reconstruction). The simplicity and efficiency of our approach is based on several novel uses of appropriate sub-structures (alpha shapes) of a three-dimensional Delaunay Triangulation, its dual the three-dimensional Voronoi diagram, and dual uses of trivariate Bernstein-Bezier forms. The boundary of the CAD model is modeled using implicit cubic Bernstein-Bezier patches, while the scalar field is reconstructed with functional cubic Bernstein-Bezier patches.

title: Output hazard-free transition tests for silicon calibrated scan based delay testing
abstract: Architectural restrictions of scan greatly limit the effectiveness of traditional scan based delay tests. It has been recently shown that additional testing for delays on short paths using fast clocks can significantly lower DPM. However, accurately obtaining the needed timing information for such tests from simulation is extremely difficult. The simulations must not only accurately account for the effects of process parameter variations, but also power supply noise and crosstalk from the excessive switching activity of scan tests. We suggest that learning signal timing information on silicon to "calibrate" such tests can be much more accurate and cost effective. However, such an approach requires that the outputs of the applied tests be hazard free to avoid learning incorrect timing due to a glitch at the output. Simulation results presented here indicate that such output hazard free test can be obtained with an average coverage only about 10% below the transition delay fault coverage for both launch-on-shift (LOS) and launch-on-capture (LOG) modes

title: Acoustic scattering on arbitrary manifold surfaces
abstract: We propose the use of surface subdivision as adaptive and higher-order boundary elements for solving a Helmholtz partial differential equation to calculate accurate acoustic scattering on arbitrary manifolds. Such acoustic transfer functions prove useful for designing and tuning hearing aid devices for hearing impaired individuals. The number of unknowns of the discretized linear system is the same as that in a linear element approach. Our results show that the accuracy of the subdivision approach is much better than that of the linear element approach.

title: An Efficient Algorithm for Determining 3-D Bi-plane Imaging Geometry
abstract: Biplane projection imaging is one of the primary methods for imaging and visualizing the cardiovascular systems in medicine. A key problem in such a technique is to determine the imaging geometry (i.e., the rotation and translation) of two projections so that the 3-D structure can be accurately reconstructed. Based on interesting observations and efficient geometric techniques, we present in this paper a new algorithmic solution for this problem. Comparing with existing optimization-based approaches, our technique yields better accuracy, has bounded execution time, and thus is more suitable for on-line applications. Our technique can easily deal with outliers for further improving the accuracy.