8440158
Improved information maximization based face and facial feature detection from real-time video and application in a multi-modal person identification system
In this paper an improved face detection method based on our previous information-based maximum discrimination approach is presented that maximizes the discrimination between face and non-face examples in a training set without using color or motion information. A short review of our previous method is given together with a description of a recent improvement of its detection speed. A person identification system has been developed that performs multi-modal person identification in real-time video based on this newly improved face detection method together with speaker identification.
8180421
Better proposal distributions: object tracking using unscented particle filter
Tracking objects involves the modeling of non-linear non-Gaussian systems. On one hand, variants of Kalman filters are limited by their Gaussian assumptions. On the other hand, conventional particle filter, e.g., CONDENSATION, uses transition prior as the proposal distribution. The transition prior does not take into account current observation data, and many particles can therefore be wasted in low likelihood area. To overcome these difficulties, unscented particle filter (UPF) has recently been proposed in the field of filtering theory. In this paper, we introduce the UPF framework into audio and visual tracking. The UPF uses the unscented Kalman filter to generate sophisticated proposal distributions that seamlessly integrate the current observation, thus greatly improving the tracking performance. To evaluate the efficacy of the UPF framework, we apply it in two real-world tracking applications. One is the audio-based speaker localization, and the other is the vision-based human tracking. The experimental results are compared against those of the widely used CONDENSATION approach and have demonstrated superior tracking performance.
6045052
Basis pursuit for tracking
This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon nonlinearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion.
14712495
One-class SVM for learning in image retrieval
Relevance feedback schemes using linear/quadratic estimators have been applied in content-based image retrieval to improve retrieval performance significantly. One major difficulty in relevance feedback is to estimate the support of target images in high dimensional feature space with a relatively small number of training samples. We develop a novel scheme based on one-class SVM, which fits a tight hyper-sphere in the nonlinearly transformed feature space to include most of the target images based on positive examples. The use of a kernel provides us an elegant way to deal with nonlinearity in the distribution of the target images, while the regularization term in SVM provides good generalization ability. To validate the efficacy of the proposed approach, we test it on both synthesized data and real-world images. Promising results are achieved in both cases.
12006939
Mode-based multi-hypothesis head tracking using parametric contours
The paper describes a probabilistic mode-based multi-hypothesis tracking (MHT) algorithm. The modes are the local maximums refined from initial samples in a parametric state space. Because the modes are highly representative, this technique allows us to use a small number of hypotheses to effectively model nonlinear probabilistic distributions. To ensure real-time tracking performance, we propose a novel parametric causal contour model and an efficient dynamic programming scheme to refine the initial contours to nearby modes. Furthermore, to overcome the common drawback of conventional MHT techniques, i.e., producing only the maximum likelihood estimates instead of the desired posterior, we introduce the highly effective importance sampling framework into MHT, and develop a novel procedure to estimate the posterior from the importance function. Experiments on a challenging real-world video sequence demonstrate that the proposed tracking technique is both robust in complex environment (e.g., clutter background and partial occlusion) and efficient in computation.
14613142
Coupled Bayesian Framework for Dual Energy Image Registration
Image registration for X-ray dual energy imaging is challenging due to the overlaid transparent layers (i.e., the bone and soft tissue) and the different appearances between the dual images acquired with X-rays at different energy spectra. Moreover, subpixel accuracy is necessary for good reconstruction of the bone and soft-tissue layers. This paper addresses these problems with a novel coupled Bayesian framework, in which the registration and reconstruction can effectively reinforce each other. With the reconstruction results, we can design accurate matching criteria for aligning the dual images, instead of treating them as multi-modality registration. Furthermore, prior knowledge of the bone and soft tissue can be exploited to detect poor reconstruction due to inaccurate registration; and hence correct registration errors in the coupled framework. A multiscale freeform registration algorithm is implemented to achieve subpixel registration accuracy. Promising results are obtained in the experiments.
535520
Detection of Entity Mentions Occuring in English and Chinese Text
In this paper, we describe an integrated approach to entity mention detection that yields a monolithic, almost language independent system. It is optimal in the sense that all categorical constraints are simultaneously considered. The system is compact and easy to develop and maintain, since only a single set of features and classifiers are needed to be designed and optimized. It is implemented using one-versus-all support vector machine (SVM) classifiers and a number of feature extractors at several linguistic levels. SVMs are well known for their ability to handle a large set of overlapping features with theoretically sound generalization properties. Data sparsity might be an important issue as a result of a large number of classes and relatively moderate training data size. However, we report results that the integrated system performs as good as a pipelined system that decomposes the problem into a few smaller sub-tasks. We conduct all our experiments using ACE 2004 data, evaluate the systems using ACE metrics and report competitive performance.
17642246
Evolving an Experience Base for Software Process Research
Since 1996 the USC Center for Software Engineering has been accumulating a large amount of software process experience through many real-client project software engineering practices. Through the application of the Experience Factory approach, we have collected and evolved this experience into an experience base (eBASE) which has been leveraged successfully for empirically based software process research. Through eBASE we have realized tangible benefits in automating, organizational learning, and strategic advantages for software engineering research. We share our rationale for creating and evolving eBASE, give examples of how the eBASE has been used in recent process research, discuss current limitations and challenges with eBASE, and what we hope to do achieve in the future with it.
10353520
Finding Self-Similarities in Opportunistic People Networks
Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.
406686
Filtering of shrew DDoS attacks in frequency domain
The shrew distributed denial of service (DDoS) attacks are periodic, bursty, and stealthy in nature. They are also known as reduction of quality (RoQ) attacks. Such attacks could be even more detrimental than the widely known flooding DDoS attacks because they damage the victim servers for a long time without being noticed, thereby denying new visitors to the victim servers, which are mostly e-commerce sites. Thus, in order to minimize the huge monetary losses, there is a pressing need to effectively detect such attacks in real-time. Unfortunately, effective detection of shrew attacks remains an open problem. In this paper, we meet this challenge by proposing a new signal processing approach to identifying and detecting the attacks by examining the frequency-domain characteristics of incoming traffic flows to a server. A major strength of our proposed technique is that its detection time is less than a few seconds. Furthermore, the technique entails simple software or hardware implementations, making it easily deployable in a real-life network environment
39358979
Multi-agent based dynamic scheduling for a flexible assembly system
This paper proposes a multi-agent based dynamic scheduling approach for a flexible assembly system. We first introduce a flexible control system developed by Intelligent Robotics and Automation Laboratory in National Taiwan University. Based on that control system, the agents can communicate with each other conveniently. A generic agent architecture is proposed to model the pieces of equipment in the flexible assembly system. With a distributed architecture, the agents make their scheduling decisions using their local rule base. The agents acquire the resources following the distributed resource allocation protocol. The scheduling complexity is reduced to meet the real-time response requirement in the applications for flexible automated production. The present work is applied to the experimental robotized flexible assembly system in the above laboratory.