View-based virtual learning and recognition of 3D object using view model obtained by motion-stereo

View-based approach for learning and recognition of 3D object and its pose detection was proved to be affective and efficient, except its high learning cost. In this research, we propose a virtual learning approach which generates learning samples of views of an object from its 3D view model obtained by motion-stereo method. From the generated learning sample views, features of high-order autocorrelation are extracted, and discriminant feature spaces for object recognition and pose detection are built. Recognition experiments on real objects are carried out to show the effectiveness of the proposed method. Caihua Wang, Ph.D.: He received his B.S. in mathematics and M.E. in electronic engineering from Renmin University of China, Beijing, China in 1983 and 1986, and his Ph. D. from Shizuoka University, Hamamatsu, Japan in 1996. He is a JST domestic fellow and is doing his post doctoral research at Electrotechnical Laboratory. His research interests are computer vision and image processing. He is a member of IEICE and IPSJ. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information.     

防火墙与入侵检测系统联动的网络安全体系分析

该文在对防火墙和入侵检测技术原理进行探讨的基础上,重点分析了防火墙与入侵检测系统联动的内在原因、功能特点和实现方式。分析结果表明,二者联动所构筑的网络安全体系既提升了防火墙的机动性和实时反应能力,又增强了入侵检测系统的阻断功能,从而全面提高网络系统的整体安全防护能力。     

基于校园网的入侵检测系统的研究

首先提出了网络安全技术所面临的新问题,从入侵检测技术的历史、定义、分类及发展趋势等几方面阐述了入侵检测技术的基本知识,并结合校园网入侵防御系统方案阐述了入侵检测技术的应用。     

浅谈分布式协同入侵检测

网络技术的不断发展,分布式计算环境的广泛采用海量存储和高带宽传输技术的普及,网络系统结构的日益复杂,都使得传统的基于单机的集中式入侵检测系统遇到了极大的挑战。传统的入侵检测技术通过在网络中放置多个传感器(探测器)收集网络状态信息,然后再把这些信息送到一个中央控制台分析处理。中央控制台的工作负荷过大,成为系统运行的瓶颈,在加上由于网络传输的延迟,探测器送给中央控制台的网络状态信息有可能不及时,这种模型在面对大规模、异构网络环境以及分布式协同攻击的情况下显得力不从心。在这样的情况下,分布式入侵检测系统(Distributed Intrusion Detection System,DIDS)应运而生,并成为目前入侵检测研究领域的一个研究热点。     

Detection algorithm for color dynamic images by multiple surveillance cameras under low luminance conditions based on fuzzy corresponding map

An objects detection algorithm for color dynamic images from two cameras is proposed for a surveillance system under low illumination. It provides automatic calculation of a fuzzy corresponding map and color similarity for lower luminance conditions, which detects little chromatic regions in CCD camera images under lower illumination and presents regions with a possibility of occlusion situation. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which occlusion regions are correctly presented. Moreover, implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan.     

利用Windows Native API调用序列和基于决策树算法的主机异常检测*

主要研究Windows平台下的异常检测方法,提出一种利用Windows Native API调用序列和基于决策树算法的主机服务进程模式抽取算法,并通过在模式中引入通配符而大大缩减了模式集的规模。进一步引入了表征模式间关系的转移概率,建立了模式序列的全局马尔可夫链模型,并给出了相应的异常检测算法。实验结果表明：该算法可以抽取一个规模较小且泛化能力较强的模式集,相应的检测算法可以有效地检测异常。     

SAR图像自动目标识别系统研究与设计*

研究了SAR图像自动目标识别系统。通过分析国际上先进的SAR自动目标识别系统的组成、算法实现、流程设计和所得到的结果,设计出SAR自动目标识别系统结构和识别流程图。系统结构主要由SAR图像特性库、特征库和模型库组成。识别流程在系统结构的基础上,分为检测、辨识和分类三步。最后给出了SAR图像自动目标识别系统的主要评价原则。     

基于图像处理的显微外科手术质量评价研究*

目前显微外科手术质量评定仍采用测角计进行手工测量,测量中有一定的随意性,测量误差大。针对该问题,提出了应用图像处理技术对显微外科接指手术进行分析和质量评价的方法。通过与手工测量进行对比,图像处理方法评价客观,测量精度高,得到了满意的结果。     

一种通用的渐变镜头检测方法*

针对渐变的特点,提出了一种基于非相邻帧差的通用渐变镜头检测方法,该方法利用非相邻帧差有效地突出了渐变的特征,利用窗口最大值准确地找到渐变的中心位置,该方法不用设置全局阈值,与具体的渐变转换效果无关,适应性强。实验表明该算法取得了良好的检测效果。     

多尺度人脸特征提取*

介绍了一种新的人脸特征提取的方法,首先在原始图像中找出人脸所在大致区域,然后对已经找出的人脸所在区域进行小波分解,并利用方块效应找出人眼及嘴部所在大致位置,最后提取眼、嘴的特征。试验结果表明,该方法对于人脸特征的提取具有较好的效果。