度量空间上的位势空间容量与Hausdorff测度

讨论了度量空间上的位势空间,并利用容量的概念建立其与Hausdorff测度之间的联系.     

单台光电经纬仪对目标稳定跟踪的研究

针对单台光电经纬仪目标提取特性短暂失去的情况下设备平稳过渡的问题,详细研究了目标在单台经纬仪中的运动轨迹。利用已有的数据对目标的运动轨迹进行分段最佳多项式逼近与外推,实现设备的平稳过渡。结果表明,此方法可以比较理想地解决目标短时间(2-3s)内丢失情况下的对目标的稳定跟踪问题。     

弱小目标检测与跟踪算法

低对比度小目标检测与跟踪算法的研究是电视跟踪领域的关键技术之一。针对弱小目标的目标特性,按USAN原则进行目标检测,利用目标的特征参数及目标运动的一致性、连续性排除噪声干扰,实现对目标的稳定跟踪。实验采用VC＋＋仿真平台验证该算法的可行性和有效性,并移植到DSP专用图像处理平台上,达到了工程的5可靠性与实时性要求。     

Dynamic target tracking with fringe-adjusted joint transform correlation and template matching

Target tracking in forward-looking infrared (FLIR) video sequences is a challenging problem because of various limitations such as low signal-to-noise ratio (SNR), image blurring, partial occlusion, and low texture information, which often leads to missing targets or tracking nontarget objects. To alleviate these problems, we developed a novel algorithm that involves local-deviation-based image preprocessing as well as fringe-adjusted joint-transform-correlation--(FJTC) and template-matching--(TM) based target detection and tracking. The local-deviation-based preprocessing technique is used to suppress smooth texture such as background and to enhance target edge information. However, for complex situations such as the target blending with background, partial occlusion of the target, or proximity of the target to other similar nontarget objects, FJTC may produce a false alarm. For such cases, the TM-based detection technique is used to compensate FJTC breaking points by use of cross-correlation coefficients. Finally, a robust tracking algorithm is developed by use of both FJTC and TM techniques, which is called FJTC-TM technique. The performance of the proposed FJTC-TM algorithm is tested with real-life FLIR image sequences.     

Infrared target detection with probability density functions of wavelet transform subbands

We report the development of a wavelet multiresolution texture-based algorithm that uses the probability density functions (PDFs) of the subband of the wavelet decomposition of an image. The moments of these pdfs are used in a clustering algorithm to segment the targets from their background clutter. Using the tools of experimental methodology, we evaluate the performance of this algorithm on real infrared imagery under varying algorithm parameter sets as well as scene, image, and false-alarm conditions. We estimate a set of multidimensional predictive analytic performance models that relate the detection probabilities as functions of false alarm, algorithm internal parameter, target pixel number, target-to-background interference ratio, target-interference ratio, and Fechner-Weber and local entropy metrics in the scene. These models can be used to predict performance in regions were no data are available and to optimize performance by selection of the optimum parameter and constant false-alarm values in regions with known scene and metric conditions.     

A similarity coefficient measure and machine-parts grouping in cellular manufacturing systems

For more than three decades, similarity coefficient measures one of the important tools for solving group technology problems have gained the attention of the research community in cellular manufacturing systems. A new similarity coefficient measure that uses a set of important characteristic properties for grouping is developed here for use as an intermediate tool to form cohesive cells. A mathematical model that uses this similarity coefficient for optimally solving the cell-formation problems in cellular manufacturing is developed. A heuristic procedure that improves the optimal methodology in term of solution capability of the large instances is devised for an efficient solution. Both the optimal methodology and the heuristic are applied to some well-known problems from literature to compare the grouping efficiencies. The similarity coefficient and the solution methodologies developed are able to solve the cell formation problems efficiently.     

智能目标检测与跟踪系统的设计与实现

为了实现复杂背景下对任意目标的智能检测与稳定跟踪,设计了基于双DSP结构的集视频处理、云台运动控制于一体的实时电视跟踪系统.系统采用模块化硬件结构,并提供直接用户接口可使系统脱离上位机独立工作.同时改进了目标识别算法,提出一种基于区域分割的相关模板提取方法.利用自适应空域滤波器分割出目标区域,再用二次投影法快速提取目标,并以模板评价函数为准则实时更新模板.具有模板自适应性强,目标判断准确,跟踪鲁棒性好等优点.外场实验表明,在目标变化和有物体遮挡干扰的情况下,系统能准确提取任意指定目标,并保持连续稳定的跟踪.     

Gabor小波目标特征提取和跟踪方法的研究

为了解决目标在发生旋转、尺度及平移等几何变形时的自动跟踪问题,提出 Sobel 边缘检测与 L-M 优化结合的 Gabor 小波目标提取与跟踪方法。该方法自动寻找目标的小波特征点,在跟踪过程中不断优化特征模板的旋转、尺度以及平移等几何变形参数,使得小波特征向量值在最小平方和意义上与初始值相匹配,实现目标跟踪。实验结果表明,该算法能够有效地提取目标特征,在目标尺度变化达到 50%,角度变化 10o,旋转 90o等不利条件下实现了可靠跟踪。     

Error-space estimation method and simplified algorithm for space target tracking

To improve space target tracking precision and the stability of mobile optoelectronic tracking equipment, an error-space estimation method based on the Kalman filter is discussed, and a simplified algorithm is presented to reduce calculation cost. Based on an available measurement of a space target without sufficient validity and accuracy, the actual position related to the tracking equipment is decomposed to an earlier offline prediction of the kinetic model method and prediction errors. By regarding prediction errors as the motion of a weak maneuver target, the errors can be estimated more accurately in error space. By synthesizing estimation of the errors and offline prediction, the space target position is obtained with higher accuracy to improve tracking performance.     

基于SSD-CF的无人艇目标检测跟踪方法

针对目前目标检测和目标跟踪算法对无人艇运算配置要求高、速度慢等问题,该文一种提出基于SSD-CF的无人艇目标检测跟踪方法。利用MobileNets结构结合SSD目标检测算法构建轻量级卷积神经网络,实现无人艇的水面目标检测。目标检测结果作为相关滤波CF目标跟踪算法的初始输入,并在目标跟踪过程的保障其有效性。通过MODD水面船只视频数据实验表明,SSD-CF方法融合目标检测与目标跟踪算法,可有效地降低对运算力的要求,提升目标检测跟踪速度和目标位置的稳定连续性。     

复杂背景下飞行目标的快速提取与跟踪

为了对飞行目标(典型目标为导弹和飞机)进行跟踪,利用差分图像结合最大空穴检出算法进行目标的提取和捕获,以模板评价函数为准则对模板进行适时更新,利用归一化的互相关系数确定目标在图像中的位置.为了提高速度,尽量采用IPP函数处理大部分的算法,相比较于传统的函数,IPP函数大幅度地提高了运算速度.实验证明,该方法能够在目标变化较大、图像模糊、对比度较低的情况下准确识别并跟踪目标,处理速度基本满足了使用要求.     

Toward establishing a unified metric for performance and learning goal orientations

Findings in the goal orientation literature are convoluted due to sample and scale dependent interpretations and potentially the use of inappropriate measurement models for ordinal data. The purpose of this investigation was to explore the possibility of developing a common metric in order to enhance communication among goal orientation researchers using different scales. A simultaneous calibration of four commonly used goal orientation scales was undertaken to investigate if item responses meet the unidimensionality requirement of Rasch models and hence provided a common metric. Participants were 305 third year psychology students at a large University in France. Results indicated that 30 of 36 learning goal and 28 of 33 performance goals items from the four scales defined unidimensional constructs. Correlations between person measures from the four scales and the simultaneous calibration for both learning goal and performance goals support the interpretation of unidimensional variables. Raw score to interval measure conversion tables provide goal orientation researchers using different scales a common metric for interpretation and statistical analyses. Limitations and extensions of this research are discussed.     

核函数带宽自适应的Mean shift目标跟踪算法

针对Mean shift核函数带宽不能实时改变的缺陷,提出一种仿射变换下的核函数带宽可变的Mean shift跟踪算法。以仿射变换来描述目标尺寸随时间的变化,分别利用Mean shift和连续两帧中匹配窗口的最大相关系数来得到仿射变换的参数。以比例参数计算出带宽的实时变化,其它参数可提供Mean shift更好的起始位置。这些工作改善了Mean shift算法在目标尺寸变化时的跟踪效果。实验证明,本算法能够有效地跟踪尺寸变化的目标,并且具有更好的实时性。     

Comparison of automatic target recognition system performance with full- and reduced-resolution correlators

The performance of an automatic target recognition (ATR) system with full- and reduced-resolution correlators was compared. In addition, the ATR system performance with reduced-resolution filter sets designed by use of multiresolution analyasis (MA) and downsampling (DS) techniques was also compared. It was discovered that results obtained at the optical correlator subsystem level, pertaining to the relative merits of the MA and the DS techniques, could not be extrapolated to the system level. This was because target signature differences between the test and the training imagery were discovered to have a greater influence on system performance than the choice of filter design technique. In addition, it was found that, for the case in which the target signature and the reduced-resolution filter were of the same size, there was some degradation in the receiver-operating-characteristic curve for reduced resolution compared with full. Nevertheless, this was deemed to have no practical significance, and thus the use of reduced-resolution optical correlators for ATR merits serious consideration.     

真假目标红外图像相似度检测算法

本文提出了真,假目标红外图像相似性定量的计算方法.该方法应用小波变换将真、假目标的红外图像分解为不用层次的子图像;利用Harris角点检测算子提取不用层次子图像的特征点,构成特征点集;并利用改进的Hausdorff距离计算出特征点集之间的Hausdorff距离.该距离越小说明真、假目标的红外图像越相似,也就说明假目标的红外示假效果越好.实验结果证明该算法能客观准确的计算出真、假目标红外图像的相似度,计算过程快速.     

非线性滤波CRLB推导及在目标跟踪中的应用

针对可叠加零均值高斯白噪声的非线性滤波,利用无迹变换,推导出理论上的误差性能下界Cramét-Rao Low Bound(CRLB).在利用光电信号对目标进行纯方位角跟踪中,提出一种次优的非线性滤波算法--距离参数化无迹卡尔曼滤波(RPUKF).仿真中将其和距离参数化扩展卡尔曼滤波(RPEKF)的性能曲线以及理论性能下界做了比较.结果表明,两种次优算法的曲线逐渐逼近理论下界,且PRUKF性能更优.     

无人机地面目标跟踪系统的建模与控制

对无人机地面目标跟踪系统进行了分析,并建立了各个子系统的数学模型,针对跟踪过程中可能导致目标丢失的两个关键问题--无人机的飞行轨迹和云台摄像机的控制,给出了一种轨迹规划与跟踪算法,该算法既可以使无人机与目标保持一定距离,又保证了无人机在飞行过程中机头始终朝向目标.此外,设计了一种云台摄像机控制方法,该方法利用无人机与目标的相对位姿和目标偏离图像中心的偏差作为反馈信息对云台摄像机进行控制.仿真结果表明本文所设计的无人机地面目标跟踪系统具有良好的性能.     

Optical correlator based target detection, recognition, classification, and tracking

A dedicated automatic target recognition and tracking optical correlator (OC) system using advanced processing technology has been developed. Rapidly cycling data-cubes with size, shape, and orientation are employed with software algorithms to isolate correlation peaks and enable tracking of targets in maritime environments with future track prediction. The method has been found superior to employing maximum average correlation height filters for which the correlation peak intensity drops off in proportion to the number of training images. The physical dimensions of the OC system may be reduced to as small as 2 in.×2 in.×3 in. (51 mm×51 mm×76 mm) by modifying and minimizing the OC components.     

The design and performance of the ZEUS global tracking trigger

The Global Tracking Trigger (GTT) of the ZEUS experiment is described. The GTT is data driven at the ZEUS first level trigger rate of and performs event-based track finding on data from the experiment's Central Tracking Detector (CTD), silicon Micro Vertex Detector (MVD) and Straw Tube Tracker (STT) forward detectors. The resulting track-based trigger quantities calculated (track multiplicity, vertices, vector meson masses, background event probabilities, etc.) are available within and are used in the experiment's second level trigger to improve the selection of physics events. Detector information is pushed to the PC farm of the GTT using PowerPC VME board computers which are either embedded within the detector's frontend readout system (MVD) or are parasitically attached to them via multiple serial transputer links (CTD and STT). Data flow and control is performed via point-to-point Fast and Giga ethernet switched network connections using the TCP protocol. The principal design challenges were: integrating new and interfacing to existing frontend systems, providing a useful trigger result, satisfying the rate and latency requirements and not interfering with ongoing data taking during commissioning. These aims have been achieved. The GTT has been actively used in the ZEUS trigger since 2004 when an initial CTD-only algorithm was used; in 2005 this was upgraded to use MVD information which significantly improves track and primary vertex resolutions. Commissioning problems delayed the STT implementation and its use in the GTT has only been tested.