Concealed weapon detection and visualization in a synthesized image

Images acquired by heterogeneous image sensors may provide complementary information about the scene, for instance, the visual image can provide personal identification information like the facial pattern while the infrared (IR) or millimeter wave image can detect the suspicious regions of concealed weapons. Usually, a technique, namely multiresolution pixel-level image fusion is applied to integrate the information from multi-sensor images. However, when the images are significantly different, the performance of the multiresolution fusion algorithms is not always satisfactory. In this study, a new strategy consisting of two steps is proposed. The first step is to use an unsupervised fuzzy k-means clustering to detect the concealed weapon from the IR image. The detected region is embedded in the visual image in the second step and this process is implemented with a multiresolution mosaic technique. Therefore, the synthesized image retains the quality comparable to the visual image while the region of the concealed weapon is highlighted and enhanced. The experimental results indicate the efficiency of the proposed approach.This material is based on part of the work carried out at the SPCR laboratory of Lehigh University and the work is partially supported by the U. S. Army Research Office under grant number DAAD19-00-1-0431. The content of the information does not necessarily reflect the position or the policy of the federal government, and no official endorsement should be inferred.     

Robust pole clustering in a good ride quality region of aircraft for matrices with structured uncertainties

This paper presents a general analysis of robust pole clustering in a good ride quality region (GRQR) of aircraft for matrices with structured uncertainties. This region is an intersection of a ring and a horizontal strip, located in the left half-plane, which is a specific non-Ω-transformable region providing good ride quality of aircraft. The paper applies the Rayleigh principle along the norm theory to analyze robust pole clustering within this region since the generalized Lyapunov theory is not valid for non-Ω-transformable regions. Concerned uncertainties are structured/parametric uncertainties, including interval matrices. The results are useful for robust control analysis and design, especially, of robust good ride quality of aircraft, shuttles, vehicles and space station, as well as some industrial systems. An example of the F-16 dynamics for which GRQR is suitable is included to illustrate the results.     

几种常用CT图像分割算法分析和探讨

图像分割是数字图像处理的一个经典难题。其分割质量的好坏直接影响到图像处理的后续工作。因而在理论研究和实际应用中受到研究人员的广泛重视。本文在总结了过去的分割方法基础上,就目前常用几种分割方法进行实验研究。主要包括阈值分割、LOG算子分割、FCM聚类分割、分水岭分割,并结合CT图像进行图像分割。文章阐述了各种分割方法的概念及原理,并在此基础上对分割结果进行了分析和探讨,得出各种方法的优点和不足。这些实验结论将为后续研究提供科学依据。     

基于模糊聚类的半自动跟踪技术研究

现有半自动跟踪系统将操作员等效为准线性环节,增大了训练的难度和成本,不适应复杂战场环境下多个快速机动目标的选取和跟踪.利用特征提取方法得到视场的“潜在目标”集,引入模糊聚类方法,通过对操作响应进行分类,建立操控意图与目标运动特征之间的关系模型.通过充分的仿真试验,对比研究了跟踪平稳性、快速性、准确性等指标,结果说明该方法能够有效提升整体跟踪性能.     

一种新的轨迹聚类及有效性评价方法

为解决轨迹聚类问题,提出一种新的无监督轨迹聚类及聚类有效性评估方法。通过建立双层字符串轨迹模型,计算得到轨迹间距离并用作聚类依据。提出轨迹同距点比例的概念,以此作为聚类工具,并采用类内平均同距点比例作为聚类有效性评价值。利用麻省理工大学(Massachusetts Institute of Technology,MIT)停车场行人路径数据集进行实验,实验结果表明,新的无监督聚类算法能较好地完成轨迹聚类任务,平均类内同距点比例能够很好地衡量分类效果。     

一种基于数据竞争的高分辨率图像的聚类分割算法

近些年,机器人技术得到了迅猛的发展,应用越来越广泛.随着机器人技术的推广和普及,对机器人使用的要求也越来越高,其中对智能机器人的要求尤显迫切.机器视觉是智能机器人研究领域的一个重要研究方向.在机器人视觉系统中,核心问题是目标提取,对目标实时、准确、快速提取的关键技术是图像分割.由于机器人感知的环境的复杂性及目标的多样性,往往导致机器人感知获得的图像数据量较大且图像本身存在不可预知的复杂性,这就对准确的目标分割和提取处理提出了挑战性问题.本文针对高分辨率图像数据集的分割处理,提出一种新的聚类算法,即根据数据点能量和的大小识别类代表点和类成员点,通过数据点间的竞争识别出最有能力成为簇成员的数据点,并将其与mean shift聚类算法有效地结合应用于彩色图像分割问题中,能够快速高效地实现高分辨率图像的目标分割,并得到较好的图像分割效果.实验结果表明,本文算法在分割效果和分割效率上明显优于传统聚类算法.     

PERFORMANCE ANALYSIS OF TASK CLUSTERING HEURISTICS FOR SCHEDULING STATIC DAGS ON MULTIPROCESSOR SYSTEM*

CASS is a task management system that provides facilities for automatic grain-size optimization and task scheduling of parallel programs on distributed memory parallel architectures. The heart of CASS is a clustering module that partitions the tasks of a program into clusters that match the granularity of the target machine. This paper describes the clustering algorithms used by CASS and compares them with the best known algorithms reported in the literature, namely the PY algorithm (for clustering with task duplication) and the DSC algorithm (for clustering with no task duplication). It is shown that the clustering algorithms used by CASS outperform both the PY and DSC algorithms in terms of both speed and solution quality.     

基于SOM和引力场聚类的金融数据可视化

平行坐标技术是信息可视化中重要的分析手段,可以实现多维数据在二维空间上的可视化.为了给用户提供一种快捷、方便的金融数据可视化及分析工具,提出一种基于引力场聚类的金融数据可视化方法.首先利用自组织映射(SOM)对初始金融数据进行分类,使每类数据都含有特定的经济意义;然后进行视觉聚类,利用引力场原理对每个类中的折线进行聚拢,对类与类之间进行排斥,再通过设置不透明度以及交互操作等手段对可视化结果进行增强.实验结果表明,该方法可以形成清晰的可视化聚类结果,便于发现数据的变化规律.     

制造资源混合粒度优化组合方案求解技术

为实现对云请求端制造需求的快速响应并提供最理想的制造云,在定义制造云服务(manufacturing cloudservice,MCS)的基础上,提出了MCS混合粒度动态优化组合方法.通过对制造服务需求的解析,将MCS按照粒度从大到小的顺序进行匹配,实现了云池资源的初次过滤;然后构建基于总服务成本、服务质量和交易期的制造云多目标优化数学模型,并引入Epsilon-SPEA2优化算法对该优化问题进行求解,从而快速高效地获得Pareto最优解;采用基于改进优劣解距离(technique for order preference by similarity to an ideal solution,TOPSIS)的动态多属性决策方法对Pareto最优解所代表的互为非支配的MCS组合方案进行评价排序,筛选出最优的MCS组合方案.最后结合某区域模具公司群所能提供的制造服务以及相应历史数据,依据客户需求进行MCS的动态优化组合仿真,验证了文中方法的可行性和实用性.     

改进近邻传播聚类的彩色图像分割

针对近邻传播(AP)聚类算法存在运算时间长、空间复杂度高而难以应用于较大规模图像数据处理的问题,提出一种将mean shift(MS)算法和AP算法相结合的彩色图像分割方法——MSAP算法.首先应用MS算法对输入目标图像进行预分割,将分割后的区域数目代替原图像像素点数目作为AP算法输入数据的规模,计算每个区域中所有像素的彩色向量平均值,并将其作为AP算法输入的数据点,选用数据点间的距离作为相似度的测度指标;然后应用AP算法在数据相似度矩阵上进行聚类,得到最终的图像分割结果.实验结果表明,与AP算法相比,MSAP算法在运行时间和分割效果方面都有显著的提高.