Automatic fog detection and estimation of visibility distance through use of an onboard camera

In this paper, we will present a technique for measuring visibility distances under foggy weather conditions using a camera mounted onboard a moving vehicle. Our research has focused in particular on the problem of detecting daytime fog and estimating visibility distances; thanks to these efforts, an original method has been developed, tested and patented. The approach consists of dynamically implementing Koschmieder's law. Our method enables computing the meteorological visibility distance, a measure defined by the International Commission on Illumination (CIE) as the distance beyond which a black object of an appropriate dimension is perceived with a contrast of less than 5%. Our proposed solution is an original one, featuring the advantage of utilizing a single camera and necessitating the presence of just the road and sky in the scene. As opposed to other methods that require the explicit extraction of the road, this method offers fewer constraints by virtue of being applicable with no more than the extraction of a homogeneous surface containing a portion of the road and sky within the image. This image preprocessing also serves to identify the level of compatibility of the processed image with the set of Koschmieder's model hypotheses. Nicolas Hautiére graduated from the École Nationale des Travaux Publics de l'État, France (2002). He received his M.S. and Ph.D. degree in computer vision, respectively, in 2002 and 2005 from Saint-Étienne University (France). From 2002, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France. His research interests include trafic engineering, computer vision, and pattern recognition. Jean-Philippe Tarel graduated from the École Nationale des Ponts et Chaussées, Paris, France (1991). He received his Ph.D. degree in Applied Mathematics from Paris IX-Dauphine University in 1996 and he was with the Institut National de Recherche en Informatique et Automatique (INRIA) from 1991 to 1996. From 1997 to 1998, he was a research associate at Brown University, USA. From 1999, he is a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC), Paris, France, and from 2001 to 2003 in the INRIA. His research interests include computer vision, pattern recognition, and shape modeling. Jean Lavenant graduated from the École Nationale des Travaux Publics de l'État, Lyon, France (2001). He received the M.S. degree in Computer Vision from Jean Monnet university of Saint-Étienne in 2001. In 2001, he was a researcher in the Laboratoire Central des Ponts et Chaussées (LCPC). In 2002, he was a system engineer in Chicago (USA). He is currently an engineer for the french ministry of transports. Didier Aubert received the M.S. and Ph.D. degree, respectively, in 1985 and 1989 from the National Polytechnical Institut of Grenoble (INPG). From 1989--1990, he worked as a research scientist on the development of an automatic road following system for the NAVLAB at Carnegie Mellon University. From 1990–1994, he worked in the research department of a private company (ITMI). During this period he was the project leader of several projects dealing with computer vision. He is currently a researcher at INRETS since 1995 and works on Road traffic measurements, crowd monitoring, automated highway systems, and driving assistance systems for vehicles. He is an image processing expert for several companies, teaches at Universities (Paris VI, Paris XI, ENPC, ENST) and is at the editorial board of RTS (Research - Transport - Safety).     

面阵凝视型成像空间应用技术

介绍了凝视型成像技术的特点、优势、现状和发展趋势，论述了凝视型成像技术应用于遥感领域发展的重要性，总结了面阵凝视型成像技术作为空间应用所涵盖的关键技术，将为我国空间凝视型成像技术的进一步发展提供参考。     

一种新颖的基于非压缩数字视频的水印盲检测算法

数字水印是一种嵌入到多媒体数据中用来进行版权标识的工具。在众多的宿主媒体中，数字视频因具有隐藏容量大、透明性好、鲁棒性强等诸多优点而受到日益广泛的关注。但是很多文献中提到的视频水印都是从数据流中提取单帧图像进行处理，这类算法与静态图像的水印方法如出一辙，没有充分利用视频文件的各种特性。而且对帧平均、视频压缩等常见的运动图像攻击方法十分敏感。针对这些问题，本文以非压缩视频文件为实验对象，结合人类视觉模型和彩色图像场景分割的方法，提出并实现了一种基于视频时间轴的数字水印盲检测算法。实验结果表明算法有效实用。     

井间地震反射波资料处理

井间地震反射波资料处理可以看成是有偏VSP反射波资料处理的一种延伸。由于一对井间地震资料相当于近千个有偏VSP，加上反射角增大和频率大幅度提高（约10倍），因此，井间地震反射波资料处理有着自身的一些特点。为此，对井问地震反射波资料处理中的一些技术和问题进行了讨论。对井间地震资料进行了分析，指出井间地震资料具有反射波场较弱、频率很高、管道波能量很强等特点；介绍了井间地震资料预处理方法，从空间属性建立、数据选排（抽道集）、道编辑、频谱分析、随机噪声压制和管波等相干噪声压制诸方面进行了讨论；对井间地震资料的波场分离和成像中需要注意的几个关键性问题进行了探讨，对于井间地震资料的高频特性给噪声压制和同相叠加处理造成的困难，通过提高叠加次数予以解决，对于大反射角对同相叠加造成的影响，则采用限角叠加技术来解决。实际资料处理表明，所采用的处理技术和措施合理有效，获得的反射波剖面品质良好。     

结合功率控制的时-空多用户检测器

针对CDMA系统中抗干扰技术,提出一种结合多用户检测、波束成形和功率控制的算法,对接收信号在时域和空间上进行联合处理,能大大抑制干扰,提高系统容量。     

一种基于HMM模型的弱小点目标的检测算法

讨论复杂背景、强干扰条件下移动平台下弱小目标的检测，提出一种基于隐Markov模型的快速检测算法，该算法通过计算摄像机视场的运动，消除背景的干扰，通过计算模型中的目标转移概率和似然函数，确定点目标的运动轨迹，实验表明了该算法的有效性。     

自由曲面五坐标数控加工干涉检查

提出了一种新方法，用于环刀五坐标数控加工的干涉检查。该方法将干涉检查分为刀轴及侧刃干涉检查和端面干涉检查。首先将加工曲面分片，选取合适的初始点。然后利用自由曲面的微分特性，确定跟踪方向和步长，迅速计算出工件和刀具的最短距离，从而判断是否发生干涉。实验仿真表明，达到同样精度时，该算法计算效率远高于常规的三角面片法。     

基于Gabor展开的水下目标激光探测的信号检测与识别

针对水下目标激光探测的信号检测与识别的问题 ,通过对回波信号的Gabor展开进行分析 ,建立了局部窗口多重相关的峰值检测方法 ,以及利用系数矩阵的迹和特征值描述目标的反射性能 ,实现了对目标的分类识别。实验表明 ,上述方法对目标的有效检测率达 93 0 % ,分类识别有效率达 83 2 %。     

基于扩展分形和CFAR特征融合的SAR图像目标识别

研究了多信息融合技术在SAR图像目标识别中的应用。将扩展分形特征(Extended Fractal)与双参数恒虚警特征(Double Parameter CFAR)形成的多信息进行融合处理。运用Dempster-Shafer证据理论，在决策层对SAR图像中的像素进行识别分类。实验结果表明通过融合对像素分类的准确性明显好于单特征的检测结果，减少了虚警概率，提高了系统的识别能力。     

线性调频信号检测方法的研究

在线性调频信号体制雷达的数字脉冲压缩系统中对数字脉压后的脉冲串进行DFT相参处理时，若采样时刻偏离脉压回波的峰值时刻，不同的采样频率使得DFT相参积累后的信噪比会有不同程度的损失。本文详细分析了采样频率分别为20MHz和10MHz时对信号相参积累的影响，并对幅值检测和M／N检测两种检测方法的检测性能进行了比较，仿真结果表明，M／N检测优于幅值检测。