17881375
Virtual Mouse - Inputting Device by Hand Gesture Tracking and Recognition
In this paper, we develop a system to track and recognize hand motion in nearly real time. An important application of this system is to simulate mouse as a visual inputting device. Tracking approach is based on Condensation algorithm, and active shape model. Our contribution is combining multi-modal templates to increase the tracking performance. Weighting value is given to the sampling ratio of Condensation by applying the prior property of the templates. The recognition approach is based on HMM. Experiments show our system is very promising to work as an auxiliary inputting device.
16132997
Manifold of facial expression
We propose the concept of manifold of facial expression based on the observation that images of a subject's facial expressions define a smooth manifold in the high dimensional image space. Such a manifold representation can provide a unified framework for facial expression analysis. We first apply active wavelet networks (AWN) on the image sequences for facial feature localization. To learn the structure of the manifold in the feature space derived by AWN, we investigated two types of embeddings from a high dimensional space to a low dimensional space: locally linear embedding (LLE) and Lipschitz embedding. Our experiments show that LLE is suitable for visualizing expression manifolds. After applying Lipschitz embedding, the expression manifold can be approximately considered as a super-spherical surface in the embedding space. For manifolds derived from different subjects, we propose a nonlinear alignment algorithm that keeps the semantic similarity of facial expression from different subjects on one generalized manifold. We also show that nonlinear alignment outperforms linear alignment in expression classification.
12971605
AAM derived face representations for robust facial action recognition
In this paper, we present results on experiments employing active appearance model (AAM) derived facial representations, for the task of facial action recognition. Experimental results demonstrate the benefit of AAM-derived representations on a spontaneous AU database containing "real-world" variation. Additionally, we explore a number of normalization methods for these representations which increase facial action recognition performance
13451430
Non-negative matrix factorization framework for face recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds both the non-negative constraint and the Fisher constraint to matrix factorization. The Fisher constraint maximizes the between-class scatter and minimizes the within-class scatter of face samples. Subsequently, FNMF improves the capability of face recognition. PNMF adds the non-negative constraint and characteristics of PCA, such as maximizing the variance of output coordinates, orthogonal bases, etc. to matrix factorization. Therefore, we can get intuitive features and desirable PCA characteristics. Our experiments show that FNMF and PNMF achieve better face recognition performance than NMF and Local NMF.
8192681
Image Retrieval Using Schwarz Representation of One-Dimensional Feature
Retrieval efficiency and accuracy are two important issues in designing a content based database retrieval system. In order to retrieve efficiently, we must extract feature to build index. Recently intensive research focused on how to extract one-dimensional features and calculate the distance between them, such as color histogram, Fourier descriptor, image shape spectrum (ISS). We develop a new method to match one-dimensional feature function in multiscale space using Schwarz representation. It can obtain closed form match function and similarity measure instead of traditional optimization. Thus we can calculate the global distance when the local information of feature function is matched. In this paper, we use the center distance function of shape as the feature functions. We calculate their Schwarz representation as indices, and calculate the optimal distance as similarity measure to sort the images. Experimental results show its efficiency and accuracy.
17727182
E-Partner: A Photo-Realistic Conversation Agent
An E-Partner is a photo-realistic conversation agent, which has a talking head that not only look photo-realistic but also can have a conversation with the user about a given topic. The conversation is multimedia-enriched in that the E-Partner presents relevant multimedia materials throughout the conversation. To address the challenges presented by the complex conversation domain and task, and to achieve adaptive behaviors, we have derived a novel dialogue manager design consisting of five parts: a domain model, a dialogue model, a discourse model, a task model, and a user model. We also extended existing facial animation techniques to create photo-realistic talking heads that facilitate conversational interactions. Some practical issues like how to handle the uncertainty from speech level are also discussed.
8404805
Fractal Dimensions for Percolation and Phase Transition Models: Theory and New Monte Carlo Calculation Method
206993144
Charge-based core and the model architecture of BSIM5
The paper outlines the charge-based core and the architecture of the BSIM5 MOSFET model for sub-100 nm CMOS circuit simulation. The BSIM5 model is a continuous, completely symmetric and accurate non charge-sheet based MOS transistor model derived from the basic device physics, including various physics effects. Comparison of the inversion charge between the BSIM5 prediction and self-consistent numerical solution shows good agreement. The demonstration of fully symmetric characteristics of BSIM5, such as channel current and its high-order derivative in the Gummel symmetry test, and charge and trans-capacitances in a SPICE simulation, also implies BSIM5 is the physically symmetric MOSFET model valid for RF-analog circuit simulations.
2350863
Generating and Applying Rules for Interval Valued Fuzzy Observations
One of the objectives of intelligent data engineering and automated learning is to develop algorithms that learn the environment, generate rules, and take possible courses of actions. In this paper, we report our work on how to generate and apply such rules with a rule matrix model. Since the environments can be interval valued and rules often fuzzy, we further study how to obtain and apply rules for interval valued fuzzy observations.
16131402
A mobile agent-based active network architecture
Active networks enable the customization of network functionality without the lengthy standard-mediated committee processes. Most of the works in the literature utilize capsules or active packets as the means to transfer code information across active networks. In this paper, we propose an active network infrastructure based on mobile agent technologies. In our prototype implementation, mobile agents are the building blocks carrying functional customizations, and the active nodes offer software application layers, caled agent servers, to process mobile agent-specific customizations to facilitate network functionality. Both integrated and discrete operational models of network customizations are supported. In addition, for the application-specific protocol development and deployment, an abstract protocol structure and a protocol loading mechanism are presented. Furthermore, we provide an agent management/control mechanism and devise a protocol management/control mechanism. As a result, improved network functionality can be achieved.
14717474
A Novel Approach to Contention Control in IEEE 802.11e-Operated WLANs
In this paper, we devise, in compliance with the IEEE 802.11e protocol, a novel MAC-centric approach, called MAC contention control (MCC), to maximizing the bandwidth utilization and achieving proportional bandwidth allocation. We first show that approaches based on estimating the number of competing nodes and then setting the contention window size may not converge (and in some cases diverge) because of network dynamics. Then, by studying the optimality condition derived in our prior work, we identify two parameters (referred to as control references) that remain approximately constant when the network operates at the optimal operational point, regardless of the number of competing nodes in each AC. We instrument MCC to measure these control references, compare measurement results to their optimal control reference levels, and adjust the packet dequeuing rate from the interface queues in an additive-increase-multiplicative-decrease (AIMD) fashion and with respect to prespecified bandwidth allocation ratio associated with its AC. In some sense, MCC controls the rate of passing packets from the interface queues to to the MAC access function, and thus practically controls the effective number of competing nodes. We have conducted an extensive simulation study, and demonstrated the superiority of MCC to 802.11e in terms of both the achievable network throughput and the capability of achieving proportional bandwidth allocation. This, coupled with the fact that MCC does not require change in firmware and can be practically deployed, makes MCC a viable approach to contention control in IEEE 802.11e-operated WLANs.