Error Concealment Based on Semantic Prioritization with Hardware‐Based Face Tracking

With video compression standards such as MPEG‐4, a transmission error happens in a video‐packet basis, rather than in a macroblock basis. In this context, we propose a semantic error prioritization method that determines the size of a video packet based on the importance of its contents. A video packet length is made to be short for an important area such as a facial area in order to reduce the possibility of error accumulation. To facilitate the semantic error prioritization, an efficient hardware algorithm for face tracking is proposed. The increase of hardware complexity is minimal because a motion estimation engine is efficiently re‐used for face tracking. Experimental results demonstrate that the facial area is well protected with the proposed scheme.     

Pedestrian detection and tracking in infrared imagery using shape and appearance

In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appearance cues. A layered representation is first introduced and a generalized expectation-maximization (EM) algorithm is developed to separate infrared images into background (still) and foreground (moving) layers regardless of camera panning. In the two-pass scheme of detecting pedestrians from the foreground layer: shape cue is first used to eliminate non-pedestrian moving objects and then appearance cue helps to locate the exact position of pedestrians. Templates with varying sizes are sequentially applied to detect pedestrians at multiple scales to accommodate different camera distances. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm that jointly exploits the shape, appearance and distance information. Experimental results with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.     

A reconfigurable VLSI-based double-lens tracking camera

Watching and tracking an object while seeing a much wider view is one of advantages of the eye system. We proposed and developed a tracking camera system that mimics the eyes by using double-lens modules. In the system, a wide view is captured through the wide-lens module, while the target in it is tracked and magnified through the telescopic lens module. Electronic circuits for tracking control are implemented onto the reconfigurable VLSI or FPGA in order to embed the parallelism in the tracking algorithm into the hardware. A successfully developed FPGA-based prototype performs high-speed tracking at the video-rate. This work was present in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007     

Global practical tracking of a class of nonlinear systems by output feedback

In this paper we address the practical tracking problem for a class of nonlinear systems by dynamic output feedback control. Unlike most of the existing results where the unmeasurable states in the nonlinear vector field can only grow linearly, we allow higher-order growth of unmeasurable states. The proposed controller makes the tracking error arbitrarily small and demonstrates nice properties such as robustness to disturbances and universal property to reference signals.     

On the Locus of Speed-Accuracy Trade-Off in Reaction Time: Inferences From the Lateralized Readiness Potential.

Lateralized readiness potentials (LRPs) were used to determine the stage(s) of reaction time (RT) responsible for speed-accuracy trade-offs (SATs). Speeded decisions based on several types of information were examined in 3 experiments, involving, respectively, a line discrimination task, lexical decisions, and an Erikson flanker task. Three levels of SAT were obtained in each experiment by adjusting response deadlines with an adaptive tracking algorithm. Speed stress affected the duration of RT stages both before and after the start of the LRP in all experiments. The latter effect cannot be explained by guessing strategies, by variations in response force, or as an indirect consequence of the pre-LRP effect. Contrary to most models, it suggests that SAT can occur at a late postdecisional stage. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Computations of Explosive Boiling in Microgravity

Dynamics of the explosive growth of a vapor bubble in microgravity is investigated by direct numerical simulation. A front tracking/finite difference technique is used to solve for the velocity and the temperature field in both phases and to account for inertia, viscosity, and surface deformation. The method is validated by comparison of the numerical results with the available analytical formulations such as the evaporation of a one-dimensional liquid/vapor interface, frequency of oscillations of capillary waves, and other numerical results. Evolution of a three-dimensional vapor nucleus during explosive boiling is followed and a fine scale structure similar to experimental results is observed. Two-dimensional simulations yield a similar qualitative instability growth.     

卡尔曼滤波器在光电经纬仪中的应用

为了解决光电经纬仪电视跟踪系统脱靶量滞后对控制系统跟踪精度及稳定性的影响，将预测滤波技术应用到光电跟踪系统中，提出了极坐标下卡尔曼滤波算法，目标模型采用等速运动并附有时间相关的随机加速度，增加了延时补偿。仿真结果表明，跟踪误差减小，当跟踪目标从视场消失时，控制系统按照预测的目标信息跟踪。     

一种弧焊机器人实时激光扫描视觉系统

研制了一种小型高精度弧焊机器人实时激光扫描视觉系统，对其各项性能进行了测试，并进行了抗电弧光干扰试验，结果表明，此系统精度高，抗干扰能力强，检测范围宽，能广泛地应秀于机器人和自动化焊接中。     

Real-time accurate hand path tracking and joint trajectory planning for industrial robots (I)

Previously, researchers raised the accuracy for a robot’s hand to track a specified path in Cartesian space mainly through increasing the number of knots on the path and the number of the path’s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot’s hand and does not change the number of the path’s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots. Foundation item: Foundation of the Robotics Laboratory, Chinese Academy of Sciences (No. RL200002) Biography of the first author: TAN Guan-zheng, Dr., professor, born in Oct. 1962, majoring in artificial intelligence, robotics and automation.     

Ranking with uncertain labels and its applications

The techniques for image analysis and classification generally consider the image sample labels fixed and without uncertainties. The rank regression problem studied in this paper is based on the training samples with uncertain labels, which often is the case for the manual estimated image labels. A core ranking model is designed first as the bilinear fusing of multiple candidate kernels. Then, the parameters for feature selection and kernel selection are learned simultaneously by maximum a posteriori for given samples and uncertain labels. The provable convergency Expectation Maximization (EM) method is used for inferring these parameters in an iterative manner. The effectiveness of the proposed algorithm is finally validated by the extensive experiments on age ranking task and human tracking task. The popular FG-NET and the large scale Yamaha aging database are used for the age estimation experiments, and our algorithm outperforms those state-of-the-art algorithms ever reported by other interrelated literatures significantly. The experiment result of human tracking task also validates its advantage over conventional linear regression algorithm. A short version of this paper appeared in ICME07.