基于角度不变的线阵推扫式CCD相机几何畸变在轨检校方法

对航天遥感相机进行在轨检校,是提高卫星产品质量,扩大产品应用效果的重要技术内容.文中研究了一种基于角度不变的线阵推扫式CCD相机几何畸变在轨检校方法.该方法利用相机外方元素对于相机视向量夹角角度影响比较小的原理,根据地面控制点和一级产品图像,求解相机视向量夹角,从中提取相机光学部件的畸变模型参数,从而实现相机内外方元素解耦.相机畸变模型采用一维3阶多项式,对焦距和主点等引起的低阶误差能够很好地吸收.该方法用在HJ-1A/B卫星的宽幅盖CCD相机几何畸变校正上,检校误差残留在2—6个像元,和参考数据精度相似,表明取得了很好的校正效果.     

Analysis and evaluation of a general camera model

A versatile General Camera Model, GCM, has been developed, and is described in detail. The model is general in the sense that it can capture both fisheye and conventional as well as catadioptric cameras in a unified framework. The camera model includes efficient handling of non-central cameras as well as compensations for decentring distortion. A novel way of analysing radial distortion functions of camera models leads to a straightforward improvement of conventional models with respect to generality, accuracy and simplicity. Different camera models are experimentally compared for two cameras with conventional and fisheye lenses, and the results show that the overall performance is favourable for the GCM.     

基于ROI的智能交通系统摄像机控制算法

针对城市道路监控的需求,提出了一种基于感兴趣区域的摄像机自适应控制方法。它作为车牌识别系统的一个辅助功能,在固定光圈的情况下,能够根据车牌识别和运动检测的反馈信息自适应地调节摄像机的快门和增益,获取较好质量的图片,从而达到提高车牌识别率的作用,特别是该方法能够大大降低强逆光和强眩光现象对采集图像质量的影响。     

基于人工神经网络的CCD数码相机颜色特征化

阐述了基于BP神经网络的数码相机特征化方法。采用不同的神经网络结构,建立了数码相机记录的RGB信息和原影像C IEXYZ色度信息之间的非线性对应关系。对NIKON D200数码相机进行了研究,通过实验得到了合理的神经网络结构为3—10—10—3。测试不同的训练样本和测试样本,达到的C IELAB平均色差和最大色差分别为1.9~2.2和6.7~7.4个色差单位。讨论了实验设备的重复性,同时,分析了样本数量对实验结果的影响。实验结果表明:对数码相机的特征化,可采用BP神经网络技术实现较高的精度。     

西红柿采摘机器人视觉系统的设计与研究

设计、开发了西红柿采摘机器人的双目立体视觉系统,为机器人自动化采摘作业提供条件。采用VFW方法进行了实时采集系统的设计;基于成熟西红柿与背景之间颜色特征的差异信息进行图像分割来识别成熟西红柿;在摄像头标定和形心匹配的基础上,通过三维立体重建获取了西红柿果实的空间位置信息。实验结果表明:视觉系统的成熟果识别率可达到98%,图像分割识别整个过程消耗平均时间0.21s;当工作距离小于500mm时,除个别奇异点,测试距离误差绝对值可控制在14mm以内,能较好满足实际工作需要。     

航空相机检测系统软件设计

为了检测航空相机工作是否正常,并可对有故障相机进行故障诊断,该文设计了一套自动测试软件系统。该系统能模拟飞机的任务管理系统与相机以ARINC429总线传输方式传输数据,并按相机记录信息的要求,提供时间、地速、高度等信息,以便控制相机的工作参数并记录相关信息,完成对航空相机性能的测试。     

Light-weight salient foreground detection for embedded smart cameras

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. Computer vision algorithms running on these camera boards should be light-weight and efficient. Considering the memory requirements of an algorithm and its portability to an embedded processor should be an integral part of the algorithm design in addition to the accuracy requirements. This paper presents a light-weight and efficient background modeling and foreground detection algorithm that is highly robust against lighting variations and non-static backgrounds including scenes with swaying trees, water fountains and rain. Compared to many traditional methods, the memory requirement for the data saved for each pixel is very small in the proposed algorithm. Moreover, the number of memory accesses and instructions are adaptive, and are decreased depending on the amount of activity in the scene. Each pixel is treated differently based on its history, and instead of requiring the same number of memory accesses and instructions for every pixel, we require less instructions for stable background pixels. The plot of the number of unstable pixels at each frame also serves as a tool to find the video portions with high activity. The proposed method selectively updates the background model with an automatically adaptive rate, thus can adapt to rapid changes. As opposed to traditional methods, pixels are not always treated individually, and information about neighbors is incorporated into decision making. The results obtained with nine challenging outdoor and indoor sequences are presented, and compared with the results of different state-of-the-art background subtraction methods. The ROC curves and memory comparison of different background subtraction methods are also provided. The experimental results demonstrate the success of the proposed light-weight salient foreground detection method.     

基于CCD的垂线坐标仪的设计

针对矿井井壁变形引起的径向位移测量问题,提出了一种基于CCD的垂线坐标仪的设计方案,给出了垂线坐标仪的结构及其在井壁变形监测系统中的布置,详细介绍了垂线坐标仪的软、硬件设计。该垂线坐标仪利用CCD采集光信号并将其转换为视频信号,采用LPC932对采集的视频信号进行二值化处理及计数,并将处理结果发送至地面计算机进行分析,从而得到矿井井壁变形引起的径向位移量。测试结果验证了该垂线坐标仪的稳定性和精确性。     

基于PhotoModeler Scanner的普通数码相机快速检校研究

随着分辨率的不断提高与价格的日益降低,数码相机已成为近景摄影测量的基本设备,而相机的准确检校是保证量测精度的关键因素.借助于PhotoModeler Scanner软件,本文实现了相机的快速准确检校,并对野外检校的稳定性和质量进行了分析,说明该检校方法简便可靠.     

嵌入式平台下的RFID数据处理终端

介绍一种以AT91SAM9260处理器和Linux嵌入式操作系统为平台的RFID信息采集与处理系统。该系统充分利用AT91SAM9260芯片丰富的标准接口资源,集门禁、考勤、监控为一体,以Linux操作系统为软件平台,完成了RFID数据信息和USB摄像头照片数据的采集、保存与TCP/IP远程数据传输等。介绍了系统的硬件组成原理、软件设计方案和实现方法,为射频识别技术在智能一卡通系统中的应用提供了一种先进的解决方案。