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.
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
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.
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.
123451361
Probabilistic expression analysis on manifolds
In this paper, we propose a probabilistic video-based facial expression recognition method on manifolds. The concept of the manifold of facial expression is based on the observation that the images of all possible facial deformations of an individual make a smooth manifold embedded in a high dimensional image space. An enhanced Lipschitz embedding is developed to embed the aligned face appearance in a low dimensional space while keeping the main structure of the manifold. In the embedded space, a complete expression sequence becomes a path on the expression manifold, emanating from a center that corresponds to the neutral expression. Each path consists of several clusters. A probabilistic model of transition between the clusters and paths is learned through training videos in the embedded space. The likelihood of one kind of facial expression is modeled as a mixture density with the clusters as mixture centers. The transition between different expressions is represented as the evolution of the posterior probability of the six basic paths. The experimental results demonstrate that the probabilistic approach can recognize expression transitions effectively. We also synthesize image sequences of changing expressions through the manifold model.
3771585
Icon-based visualization of large high-dimensional datasets
High dimensional data visualization is critical to data analysts since it gives a direct view of original data. We present a method to visualize large amount of high dimensional data. We divide dimensions of data into several groups. Then, we use one icon to represent each group, and associate visual properties of each icon with dimensions in each group. A high dimensional data record will be represented by multiple different types of icons located in the same position. Furthermore, we use summary icons to display local details of viewer's interests and the whole data set at meantime. We show its effectiveness and efficiency through a case study on a real large data set.
146722
Adaptive dissemination of dynamic information services in an extended data broadcast environment
Considering that most information broadcast services generate data dynamically in practice, we devise in this paper an extended data broadcast mechanism which supports not only the original data broadcast scenario but also the dissemination of dynamic inforamtion services. Furthermore, we design an on-line loan-based slot allocation and feedback technique to tolerate the dynamic changes of broadcast traffic and accordingly perform the adaptation of service classification, bandwidth allocation and broadcast schedule so as to enhance the performance. Our experimental studies show that the extended data broadcast mechanism associated with the loan-based feedback technique is able to achieve a substantial saving in message traffic for dynamic information dissemination in wireless networks.
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.
8145594
Effects of global interconnect optimizations on performance estimation of deep submicron design
In this paper, we quantify the impact of global interconnect optimization techniques that address such design objectives as delay, peak noise, delay uncertainty due to noise, power, and cost. In doing so, we develop a new system-performance simulation model as a set of studies within the MARCO GSRC Technology Extrapolation (GTX) system. We model a typical point-to-point global interconnect and focus on accurate assessment of both circuit and design technology with respect to such issues as inductance, signal line shielding, dynamic delay, buffer placement uncertainty and repeater staggering. We demonstrate, for example, that optimal wire sizing models need to consider inductive effects-and that use of more accurate (-1,3) worst-case capacitive coupling noise switch factors substantially increases peak noise estimates compared to traditional (0,2) bounds. We also find that optimal repeater sizes are significantly smaller than conventional models would suggest, especially when considering energy-delay issues.
44360962
Parallel Solution of the Poisson-Boltzmann Equation for Proteins
A fortran code PBSOLVE is created for numerical solution of a linear Poissson-Boltzman equation. Finite difference discretization and successive overrelaxation (SOR) iterations on the sequence of grids are used to obtain the approximate electrostatic potential on the grid. Error control is provided by comparison of solution on nested grids. A parallel versioon of the PBSOLVE which exploits the message passing interface (MPI) has also been constructed. The performances of the program and the efficiency of parallelization have been tested on the small peptide Met-Enkephalin.