1952111
Manifold Based Analysis of Facial Expression
We propose a novel approach for modeling, tracking and recognizing facial expressions. Our method works on a low dimensional expression manifold, which is obtained by Isomap embedding. In this space, facial contour features are first clustered, using a mixture model. Then, expression dynamics are learned for tracking and classification. We use ICondensation to track facial features in the embedded space, while recognizing facial expressions in a cooperative manner, within a common probabilistic framework. The image observation likelihood is derived from a variation of the Active Shape Model (ASM) algorithm. For each cluster in the low-dimensional space, a specific ASM model is learned, thus avoiding incorrect matching due to non-linear image variations. Preliminary experimental results show that our probabilistic facial expression model on manifold significantly improves facial deformation tracking and expression recognition.
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.
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.
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.
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.
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.
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.
7704531
A TCAM-based distributed parallel IP lookup scheme and performance analysis
Using ternary content addressable memory (TCAM) for high-speed IP address lookup has been gaining popularity due to its deterministic high performance. However, restricted by the slow improvement of memory accessing speed, the route lookup engines for next-generation terabit routers demand exploiting parallelism among multiple TCAM chips. Traditional parallel methods always incur excessive redundancy and high power consumption. We propose in this paper an original TCAM-based IP lookup scheme that achieves both ultra-high lookup throughput and optimal utilization of the memory while being power-efficient. In our multi-chip scheme, we devise a load-balanced TCAM table construction algorithm together with an adaptive load balancing mechanism. The power efficiency is well controlled by decreasing the number of TCAM entries triggered in each lookup operation. Using four 133 MHz TCAM chips and given 25% more TCAM entries than the original route table, the proposed scheme achieves a lookup throughput of up to 533 MPPS while remains simple for ASIC implementation
14957395
A statistical performance simulation methodology for VLSI circuits
A statistical performance simulation (SPS) methodology for VLSI circuits is presented. Traditional methods of worst-case corner analysis lack accuracy and Monte-Carlo simulations cannot be applied to VLSI circuits because of their complexity. SPS methodology is accurate because no statistical information about the device parameter variation is lost. It achieves efficiency by analyzing the smaller circuit blocks and generating the performance distribution for the entire circuit. Circuit evaluation at any specified performance level is possible.
6208845
Efficient generation of delay change curves for noise-aware static timing analysis
In this paper, we explore the concept of using analytical models to efficiently generate delay change curves (DCCs) that can then be used to characterize the impact of noise on any victim/aggressor configuration. Such an approach captures important noise considerations such as the possibility of delay change even when the switching windows of neighboring gates do not overlap. The technique is model-independent, which we demonstrate by using several crosstalk noise models to obtain results. Furthermore, we extend an existing noise model to more accurately handle multiple aggressors in the timing analysis framework. DCC results from the analytical approach closely match those from time-consuming SPICE simulations, making timing analysis using DCCs efficient as well as accurate.
35202136
Supporting Problem Solving with Case-Stories Learning Scenario and Video-based Collaborative Learning Technology
In this paper, we suggest that case-based resources, which are used for assisting cognition during problem solving, can be structured around the work of narratives in social cultural psychology. Theories and other research methods have proposed structures within narratives and stories which may be useful to the design of case-based resources. Moreover, embedded within cases are stories which are contextually rich, supporting the epistemological groundings of situated cognition. Therefore the purposes of this paper are to discuss possible frameworks of case-stories; derive design principles as to “what” constitutes a good case story or narrative; and suggest how technology can support story-based learning. We adopt video-based Computer-Supported Collaborative Learning (CSCL) technology to support problem solving with casestories learning scenarios. Our hypothesis in this paper is that well-designed case-based resources are able to aid in the cognitive processes undergirding problem solving and meaning making. We also suggest the use of an emerging video-based collaborative learning technology to support such an instructional strategy.