14315679
Hierarchical MRF model for model-based multi-object tracking
To track multiple objects, top-down (model-based methods) and bottom-up (multi-layer analysis) methods have been proposed separately. A hierarchical MRF model is proposed to integrate these two trends into a MAP framework for tracking non-rigid objects such as human hands or faces. Parametric models of color, shape and frame difference for both foreground and background are given. Dynamic constraints are used to update the observation models and present an initial segmentation for the new frame. A novel hierarchical MRF model is proposed to efficiently refine the segmentation based on local smoothness constraints. The algorithm does not need to initialize and can detect new moving objects and track them. It can also handle the stopped objects because of the utilization of spatial-temporal constraints. Promising results are reported.
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.
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.
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.
17642380
Evolutionary Modeling of Ordinary Differential Equations for Dynamic Systems
This paper presents a new idea of modeling one-dimensional dynamic systems by higher-order ordinary differential equation (HODE) models in stead of by the ARMA models used in the traditional time series analysis. Accordingly, based on the idea of hybrid evolutionary modeling, we modify the HEMA algorithm in (Cao et al., 1998a; Cao et al., 1998b) to approach the modeling problem of HODEs for dynamic systems. Two applications of time series are used to demonstrate its effectiveness and advantages.
19799763
An approach to isochronous slot reuse for multimedia services on DQDB network
This paper discusses an isochronous connection management protocol and its slot-reuse scheme for multimedia services on DQDB network. For asynchronous services, slot-reuse improves slot utilization, which has already been discussed in depth. However only few literature addressed the slot-reuse for synchronous services. Our proposed scheme reuses video slots for either synchronous or asynchronous services according to the need of downstream nodes. Simulation shows that the scheme significantly increases the maximum number of video connections as well as reduces the failure rate of connection request comparing with that without slot-reuse.
207108480
Weakest Specifunctions for BSP
This paper introduces a calculus of weakest specification for supporting reuse of established components in deriving a design (in the sense of formal methods). The weakest specifunction generalizes the notions of weakest pre-specification and weakest parallel environment; but instead of calculating the weakest required component of a target specification, it calculates the weakest specification function whose value refines the target when applied to an established component. In particular it overcomes the restriction of those other calculi to taking merely one required component at a time. The theory of specifunctions is applied to a new weakest-design calculus in the context of BSP. The calculus is based on the par-seq specifunction which involves two required components: it places one established component in parallel with one required component and the result in sequence with another required component to meet a given specification. A calculus is provided for the par-seq specifunction and it is applied to the derivation of a distributed BSP algorithm for greatest common divisor.
17662809
Hierarchical Organisation of Predicate-Semantic Models
This paper introduces techniques to organise predicate- semantic models in a hierarchical structure so that a new model can inherit the laws of the existing ones. Generic composition is used to simplify the manipulation of predicates. Necessary restrictions are imposed on the definition of each model so that the inheritance relation can be established by checking a few conditions on the healthiness conditions and the commands. Much of the checking can be supported by laws of generic composition. The techniques also help simplify the proof of well-definedness of program combinators (i.e. their closure in the semantic space) so that we no longer need to prove it (in predicate calculus) for every new model.
2252308
Cost-Driven Hybrid Configuration Prefetching for Partial Reconfigurable Coprocessor
Reconfigurable computing systems have developed the capability of changing the configuration of the reconfigurable coprocessor multiple times during the course of a program. However, in most systems the reconfigurable coprocessor wastes computation cycles while waiting for the reconfiguration to complete. Therefore, the high demand for frequent run-time reconfiguration directly translates into higher reconfiguration overhead. Some studies have introduced the concept of prefetching to reduce the reconfiguration overhead. However, these prefetching algorithms are probability-driven. We believe that including configuration size information in the prediction algorithm directly links the training of the predictor with the performance gain. Therefore we proposed a performance-oriented cost-driven algorithm for coarse-grained configuration prefetching. Our cycle accurate simulation results show that the proposed cost-driven algorithm outperforms the probability-driven predictor by 10.8% to 29.6% in reducing reconfiguration overhead.