Subject-independent natural action recognition
A primitive-based dynamic Bayesian networks are proposed for subject-independent natural action recognition. Inferred by high-level knowledge, primitives are distinctive features that describe the context information and the motion information representing human action as well as pose. Dynamic Bayesian networks could fuse multi-information so that many kinds of weak information could function as strong information for inference. The experimental results show that primitive-based dynamic Bayesian networks not only increase the recognition rate but also improve the robustness.

title: Building the Software Infrastructure for Smart Classroom: From Open Agent Architecture (OAA) to Smart Platform
abstract: Smart Classroom is a Smart Space developed to enhance tele-education practice. This paper describes the considerations of bringing forward Smart Platform as the software infrastructure of Smart Classroom system, abandoning the former implementation based on OAA. As a multi-agent system for Smart Space, Smart Platform encompasses following features: spontaneous discovery of runtime environment, automatic management and resolving of agent dependencies, combination of delegated communication and peer-to-peer communication, etc. In addition, a surrogate agent on Smart Platform and Metaglue from MIT is now being developed to inter-connect Smart Classroom with MIT's Intelligent Room to extend their functionality.

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: A Speaker Tracking Algorithm Based on Audio and Visual Information Fusion Using Particle Filter
abstract: Object tracking by sensor fusion has become an active research area in recent years, but how to fuse various information in an efficient and robust way is still an open problem. This paper presents a new algorithm for tracking speaker based on audio and visual information fusion using particle filter. A closed-loop architecture with reliability of each individual tracker is adopted, and a new method for data fusion and reliability adjustment is proposed. Experiments show the new algorithm is efficient in fusing information and robust to noise.

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: Smart Remote Classroom: Creating a Revolutionary Real-Time Interactive Distance Learning System
abstract: Real-time interactive virtual classroom is an important type of distance learning. However currently available systems are insufficient in the support of large-scale user access, and neither can they accommodate accessing with heterogeneous devices and networks. Furthermore, these systems are usually desktop-based, which results that the teachers' experience is completely different from teaching in a real classroom. The Smart Remote Classroom project tackles these difficulties through the development of the following technologies: 1) A hybrid application-layer Multicast protocol called TORM and an Adaptive Content Delivery scheme called AMTM. Together they enable large-scale users to access a virtual classroom with different devices and networks at the same time. 2) A dedicated software called SameView, which take use of the proposed TORM and AMTM technology and provide a rich set of functions for teachers and students to efficiently carry out the real-time interactive distance learning. Moreover, SameView can record the whole process of a class into a compound multimedia document for later retrieval and review of the class. 3) An augmented classroom called Smart Classroom, where the user interface of the SameView for the teacher are distributed in and fused with the room space. Thus the teacher can instruct the remote students just like teaching face-to-face in a conventional classroom. All these technologies has been successfully integrated and demonstrated in the prototype system.

title: Delay Defect Characterization Using Low Voltage Test
abstract: For nanometer designs, many subtle defects lead to excessive delays in signal paths that cause reliability concerns. Traditional test-based diagnosis methods can only identify the failing nodes without the capability to tell the defect nature behind the observed delay faults. This differentiation is important for gathering accurate defect statistics for process improvement during yield ramp-up. In this paper we presented an effective delay defect analysis methodology that can quickly categorize the delay defects into either transistor related defects or resistive interconnect defects. The new delay defect/failure characterization method is based on low voltage test and delay defect detection in slack interval (DDSI) method. Experimental results were presented to validate the effectiveness of the new method. Practical considerations were also addressed for adoption of the methodology.

title: Efficient Algorithms for Determining 3-D Bi-Plane Imaging Geometry
abstract: Biplane projection imaging is one of the primary methods for imaging and visualizing the cardiovascular system in medicine. A key problem in such a technique is to determine the imaging geometry (i.e., the relative 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 new algorithmic solutions for this problem. Comparing with existing optimization-based approaches, our techniques yield better accuracy and have bounded execution time, thus is more suitable for on-line applications. Our techniques can easily detect outliers to further improve the accuracy.

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: Reconstructing Surfaces and Functions on Surfaces from Unorganized Three-Dimensional Data
abstract: Creating a computer model from an existing part is a common problem in reverse engineering. The part might be scanned with a device like the laser range scanner, or points might be measured on its surface with a mechanical probe. Sometimes, not only the spatial location of points, but also some associated physical property can be measured. The problem of automatically reconstructing from this data a topologically consistent and geometrically accurate model of the object and of the sampled scalar field is the subject of this paper. The proposed algorithm can deal with connected, orientable manifolds of unrestricted topological type, given a sufficiently dense and uniform sampling of the object's surface. It is capable of automatically reconstructing both the model and a scalar field over its surface. It uses Delaunay triangulations, Voronoi diagrams, and α-shapes for efficiency of computation and theoretical soundness. It generates a representation of the surface and the field based on Bernstein—Bézier polynomials, with the surface modeled by implicit patches (A-patches), that are guaranteed to be smooth and single-sheeted.

title: Convergence analysis of a discretization scheme for Gaussian curvature over triangular surfaces
abstract: In this paper, we study the convergence property of a well known discretized scheme for approximating Gaussian curvature, derived from Gauss-Bonnet theorem, over triangulated surface. Suppose the triangulation is obtained from a sampling of a smooth parametric surface, we show theoretically that the approximation has quadratic convergence rate if the surface sampling satisfies the so-called parallelogram criterion. Numerical results which justify the theoretical analysis are also presented.