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.
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.
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.
31770762
Hierarchical Segmentation and Representation with Dynamic Link Architecture Neural Network
A segmentation scheme based on tracing objects through scale space is proposed. For analyzing the image structure in scale space, a hierarchical neuraloscillator network is proposed. Its intrinsic dynamics provides a natural and reliable representation of multiple segmented patterns. The pyramid-like structure and the local interactions between the neurons can easily integrate the local context information of pixels and global image structure. This model assumes that neural cells show oscillatory activities and the segmentation result is coded by synchronization of activities. This representation framework for neural network suggested by time correlation theory receives direct experimental support from neurophysiology experiment. Computer simulations demonstrate that the network can detect the topological character of images within several oscillatory cycles. Experiment on real image showed the model's ability of segmentation.
26820236
3-D IFS fractals as real-time graphics model
Abstract Fractals have recently been attracting increasing research interest. Unlike conventional geometrical figures, fractals have unlimited details—a potentially useful feature for artistic images. One way to generate fractals is to use Iterated Function Systems (IFS). There have been significant research efforts on 2-D IFS. Programs for rendering 2-D IFS fractals are readily available. 3-D IFS appear to be a natural extension but remain an area to be investigated. This paper explores the use of 3-D IFS for creating fascinating fractal scenes. An efficient coloring, lighting and mist effects scheme is proposed. A program using the proposed scheme has been developed that can render delicate scenes consisting of multiple IFS fractals in real-time on the PC.
18836133
On the epigenetic regulation of the human reelin promoter
Reln mRNA and protein levels are reduced by approximately 50% in various cortical structures of post-mortem brain from patients diagnosed with schizophrenia or bipolar illness with psychosis. To study mechanisms responsible for this down-regulation, we have analyzed the promoter of the human reelin gene. We show that the reelin promoter directs expression of a reporter construct in multiple human cell types: neuroblastoma cells (SHSY5Y), neuronal precursor cells (NT2), differentiated neurons (hNT) and hepatoma cells (HepG2). Deletion constructs confirmed the presence of multiple elements regulating Reln expression, although the promoter activity is promiscuous, i.e. activity did not correlate with expression of the endogenous gene as reflected in terms of reelin mRNA levels. Co-transfection of the -514 bp human reelin promoter with either Sp1 or Tbr1 demonstrated that these transcription factors activate reporter expression by 6- and 8.5-fold, respectively. Within 400 bp of the RNA start site there are 100 potential CpG targets for DNA methylation. Retinoic acid (RA)-induced differentiation of NT2 cells to hNT neurons was accompanied by increased reelin expression and by the appearance of three DNase I hypersensitive sites 5' to the RNA start site. RA-induced differentiation was also associated with demethylation of the reelin promoter. To test if methylation silenced reelin expression, we methylated the promoter in vitro prior to transfection. In addition, we treated NT2 cells with the methylation inhibitor aza-2'-deoxycytidine and observed a 60-fold increase in reelin mRNA levels. The histone deacetylase inhibitors trichostatin A (TSA) and valproic acid also induced expression of the endogenous reelin promoter, although TSA was considerably more potent. These findings indicate that one determinant responsible for regulating reelin expression is the methylation status of the promoter. Our data also raise the interesting possibility that the down-regulation of reelin expression documented in psychiatric patients might be the consequence of inappropriate promoter hypermethylation.
7510335
Intelligent Sensor Fusion And Learning For Autonomous Robot Navigation
ABSTRACT This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustnes...
45017187
An Agent-Based Metric for Quality of Services over Wireless Networks
In a wireless LAN environment, wireless stations with the strongest received signal can not be guaranteed to have the best quality of service if the population sharing the network capacity was not considered. In other words, within the same access point, the more the population, the less the shared bandwidth, and the worse the quality of service will be. In this paper, we proposed an Anticipative Agent Assistance which is an agent-based metric for evaluating and managing the resource of the wireless access points, computing the potential AP list, and providing clients with resource information of APs. We also propose a novel QoS feedback mechanism which allows users to promptly adjust the service quality with AAA according to the throughput and delay requirements. We evaluate the performance of our proposed method using the ns-2 simulator. Numerical results show that AAA help reduce the transmission delay, increase the throughput, improve the network utilization, accommodate more users, and provide load-balancing.
5374807
Automated Planning and Scheduling using Calculus of Variations in Discrete Space
In this paper, we propose new dominance relations that can speed up significantly the solution process of planning problems formulated as nonlinear constrained dynamic optimization in discrete time and space. We first show that path dominance in dynamic programming cannot be applied when there are general constraints that span across multiple stages, and that node dominance, in the form of Euler-Lagrange conditions developed in optimal control theory in continuous space, cannot be extended to that in discrete space. This paper is the first to propose efficient node-dominance relations, in the form of local saddle-point conditions in each stage of a discrete-space planning problem, for pruning states that will not lead to locally optimal paths. By utilizing these dominance relations, we present efficient search algorithms whose complexity, despite exponential, has a much smaller base as compared to that without using the relations. Finally, we demonstrate the performance of our approach by integrating it in the ASPEN planner and show significant improvements in CPU time and solution quality on some spacecraft scheduling and planning benchmarks.