6062616
Estimating face pose by facial asymmetry and geometry
A robust pose estimation approach is proposed by combining facial appearance asymmetry and 3D geometry in a coarse-to-fine framework. The rough face pose is first estimated by analyzing the asymmetry of the distribution of the facial component detection confidences on an image, which actually implies an intrinsic relation between the face pose and the facial appearance. Then, this rough face pose, as well as error bandwidth, is utilized into a 3D-to-2D geometrical model matching to refine the pose estimation. The proposed approach is able to track a face with fast motion in front of cluttered background and recover its pose robustly and accurately in real- time. Experiment results are provided to demonstrate its efficiency and accuracy.
834526
Efficient 3D reconstruction for face recognition
Face recognition with variant pose, illumination and expression (PIE) is a challenging problem. In this paper, we propose an analysis-by-synthesis framework for face recognition with variant PIE. First, an efficient two-dimensional (2D)-to-three-dimensional (3D) integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination. Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace. Finally, face recognition is conducted based on these representative virtual faces. Compared with other related work, this framework has following advantages: (1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; (2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and (3) compared with other 3D reconstruction approaches, our proposed 2D-to-3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with changing PIE.
13831969
The Communication Performance of the Cray T3D and its Effect on Iterative Solvers
On many distributed memory systems, such as workstation clusters or the Intel iPSC/860, the multigrid algorithm suffers from having extensive communication requirements and, in general, it is not very competitive in comparison to the conjugate gradient algorithm. This is in contrast to the sequential problem whereby the multigrid algorithm is very effective in reducing the global residual, particularly for very large linear systems of equations. These two algorithms are now compared on the Cray T3D for solving very large systems of linear equations (resulted from grids of the order 2563 cells). The communication performance of the Cray T3D is first measured by the standard ping-pong tests and also by practical communication tasks that are found frequently in CFD calculations. It is found that the Cray T3D has a low latency (≈ 6 μs) and a high bandwidth interprocessor communication (120 MB/s) when the low-level intrinsic communication routines are used. As a result, the multigrid algorithm is found to be very competitive when compared with the conjugate gradient algorithm for solving the very large linear systems arising from the Direct Numerical Simulation of turbulent Combustion (DNSC). Results are contrasted by those on the Intel iPSC/860.
36322621
An improved diffusion algorithm for dynamic load balancing
Abstract Diffusion type algorithms [1, 3, 11] are some of the most popular algorithms for scheduling in dynamic load balancing. It is known however that this type of algorithm can suffer from slow convergence. In this paper the performance of the diffusion type algorithms is improved, while retaining the nearest neighbour communication requirement, through the use of Chebyshev polynomials. It is also proved that both the diffusion algorithm and the improved diffusion algorithm have an optimal property in terms of the amount of load migrated. Numerical results are given comparing the algorithm with the diffusion algorithm as well as a fast algorithm that requires global communication.
14073796
Salient Region Detection Using Weighted Feature Maps Based on the Human Visual Attention Model
Detection of salient regions in images is useful for object based image retrieval and browsing applications. This task can be done using methods based on the human visual attention model [1], where feature maps corresponding to color, intensity and orientation capture the corresponding salient regions. In this paper, we propose a strategy for combining the salient regions from the individual feature maps based on a new Composite Saliency Indicator (CSI) which measures the contribution of each feature map to saliency. The method also carries out a dynamic weighting of individual feature maps. The experiment results indicate that this combination strategy reflects the salient regions in an image more accurately.
28386762
Taxonomy Building and Machine Learning Based Automatic Classification for Knowledge-Oriented Chinese Questions
In this paper, we propose a taxonomy for knowledge-oriented question, and study the machine learning based classification for knowledge-oriented Chinese questions. By knowledge-oriented questions, we mean questions carrying information or knowledge about something, which cannot be well described by previous taxonomies. We build the taxonomy after the study of previous work and analysis of 6776 Chinese knowledge-oriented questions collected from different realistic sources. Then we investigate the new task of knowledge-oriented Chinese questions classification based on this taxonomy. In our approach, the popular SVM learning method is employed as classification algorithm. We explore different features and their combinations and different kernel functions for the classification, and use different performance metrics for evaluation. The results demonstrate that the proposed approach is desirable and robust. Thorough error analysis is also conduced.
15953152
Understanding network delay changes caused by routing events
Network delays and delay variations are two of the most important network performance metrics directly impacting real-time applications such as voice over IP and time-critical financial transactions. This importance is illustrated by past work on understanding the delay constancy of Internet paths and recent work on predicting network delays using virtual coordinate systems. Merely understanding currently observed delays is insufficient, as network performance can degrade not only due to traffic variability but also as a result of routing changes. Unfortunately this latter effect so far has been ignored in understanding and predicting delay related performance metrics of Internet paths. Our work is the first to address this short coming by systematically analyzing changes in network delays and jitter of a diverse and comprehensive set of Internet paths. Using empirical measurements, we illustrate that routing changes can result in roundtrip delay increase of converged paths by more than 1 second. Surprisingly, intradomain routing changes can also cause such large delay increase.
 Given these observations, we develop a framework to analyze in detail the impact of routing changes on network delays between end-hosts. Using topology information and properties associated with routing changes, we explain the causes for observed delay fluctuations and more importantly identify routing changes that lead to predictable effects on delay-related metrics. Using our framework, we study the predictability of delay and jitter changes in response to both passively observed interdomain and actively measured intradomain routing changes.