A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

Joint spatial registration and multi-target tracking using an extended PM-CPHD filter

An extended product multi-sensor cardinalized probability hypothesis density (PM-CPHD) filter for spatial registration and multi-target tracking (MTT) is proposed. The number and states of targets and the biases of sensors are jointly estimated by this method without the data association. Monte Carlo (MC) simulation results show that the proposed method (i) outperforms, although computationally more expensive than, the extended multi-sensor PHD filter which has been proposed for joint spatial registration and MTT; (ii) outperforms the multi-sensor joint probabilistic data association (MSJPDA) filter which is also extended in this study for joint spatial registration and MTT when the clutter is relatively dense.     

An iterative linear quadratic regulator based trajectory tracking controller for wheeled mobile robot

We present an iterative linear quadratic regulator(ILQR) method for trajectory tracking control of a wheeled mobile robot system.The proposed scheme involves a kinematic model linearization technique,a global trajectory generation algorithm,and trajectory tracking controller design.A lattice planner,which searches over a 3D(x,y,θ) configuration space,is adopted to generate the global trajectory.The ILQR method is used to design a local trajectory tracking controller.The effectiveness of the proposed method is demonstrated in simulation and experiment with a significantly asymmetric differential drive robot.The performance of the local controller is analyzed and compared with that of the existing linear quadratic regulator(LQR) method.According to the experiments,the new controller improves the control sequences(v,ω) iteratively and produces slightly better results.Specifically,two trajectories,’S’ and ’8’ courses,are followed with sufficient accuracy using the proposed controller.     

运动目标检测与跟踪的DSP实现

研究了运动目标检测与跟踪的DSP（DigitalSignalProcessor）实现算法,以形心跟踪算法为整个处理系统的核心。采用目标形心跟踪算法,通过目标分割阶段的目标标记,如目标面积、周长、形心位置等信息的提取建立目标跟踪波门,实现目标的连续跟踪,并将此算法移植到sEED—VPM642硬件平台,实验结果表明能够达到预定目标。此外,为了克服形心算法的准确性和实时性缺陷,采用粒子滤波对算法进行必要的扩展,从MATLAB的仿真结果看,除个别采样点存在误差较大的情况,真实值曲线与粒子滤波跟踪曲线拟合较好。     

医用药剂中可见异物自动化检测研究

针对国内人工药品检测的低效率、低准确率的不足,文章介绍了一套根据国家行业标准设计的基于智能机器视觉的可见异物自动化检测系统。该系统主要包括机械传动模块、电气控制模块、图像识别与处理模块。根据异物运动连续性和噪声运动无序性等特点,提出了改进的三次差分和能量累积算法来提取运动杂质,然后采用基于基于SIFT特征匹配和最邻近匹配的目标跟踪算法对杂质进行跟踪,获取杂质的大小和数目并分析出杂质总类,最后检测出杂质情况。最后把检测结果与国家标准进行比对,判断出该药剂是否合格。测试表明,系统的检测准确率达90%以上,满足企业要求,基本能替代人工检测。     

新的e修正鲁棒自适应控制方法在航空发动机上的应用

针对航空发动机模型的结构和参数的不确定性,提出一种基于非李亚普诺夫方程(NQLF)的e修正鲁棒模型参考自适应控制法。该方法无需知道发动机精确数学模型,抗干扰能力强,在输出误差信号的作用下具有比普通方法更快的收敛性,振荡性能够得到很好的控制。从仿真结果可见控制器能使系统比较好地跟踪参考模型的输出,对发动机模型参数和结构的变化有一定的适应能力,鲁棒性较好。     

基于改进HBA算法的生鲜闭环供应链网络鲁棒优化设计

针对生鲜闭环供应链网络设计问题,建立了一种基于生鲜闭环供应链网络的鲁棒优化模型,以解决供应链网络中的不确定性问题。首先,针对涵盖五个节点的生鲜供应链网络结构建立了多周期、多产品,以最小化成本、最小环境影响为目标的混合整数规划模型,采用模糊折中规划与区间数据鲁棒优化方法进行处理;其次,在原有蜜獾算法的基础上引入差分进化原则,增强算法的全局搜索能力与收敛速度;最后,通过MATLAB数值分析与仿真实例表明,所提鲁棒优化模型与蜜獾算法在求解生鲜闭环供应链网络设计问题中具有明显优势。     

一种低复杂度的鲁棒自适应波束形成算法

传统基于干扰噪声协方差矩阵（interference-plus-noise covariance matrix,INCM）重构的鲁棒自适应波束形成（robust adaptive beamformer,RAB）算法在多种样本数据协方差矩阵误差和信号导向向量误差的失配环境中具有较强的鲁棒性,但目前主流的INCM重构法都是对信号和干扰的导向向量通过建立凸优化模型来估计,这带来了很高的计算复杂度。为了解决这个问题,提出了一种低复杂度的基于INCM重构的RAB算法。该算法首先将干扰信号的导向向量分解为对应标称项和误差项的和,然后通过一种子空间方法估计得到误差项的单位向量。接下来对一个Capon空间谱功率最大问题进行求解,得到误差项的模值,以此得到重构的INCM。同时利用Capon空间谱中残差噪声的存在,使用交替投影法估计得到期望信号的导向向量,最后得到所提算法的权重向量。仿真实验表明所提算法在多种误差环境下具有较强鲁棒性的同时,还具有较低的计算复杂度。