Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.     

基于多镜头的视频行为识别方法

该文提出一种基于多摄像机信息融合的行为识别方法。新方法使用背景融合的方式融合多镜头的信息,使用背景差分提出人体目标,基于运动惯性定律解决了多目标跟踪的问题,并在行为识别中引入了人工智能和多层次分析等先进的手段。     

基于三阶帧差的运动特征选取方案

为解决行为识别中运动特征提取的问题,该文提出了一种基于三阶帧差的运动特征提取方案,用于提取运动特征中的变化大小、运动变化的一致性、运动方向的一致性等。实验证明,新方法速度快,准确度高。     

基于k-最近邻的支持向量预选取方法

在所有的训练样本中只有支持向量(SVs)能对支持向量机分界面优化结果产生显著影响.基于七一最近邻规则.提出了一种训练样本的预选取方法.针对一些典型人工数据集、公用基准数据集以及TM遥感数据的实验结果表明.该方法能够有效减少训练样本数目.显著加快学习速度,并保证理想的分类精度.     

一种过程支持向量机及其在动态模式分类中的应用

针对一般SVM在机制上难以直接对动态模式进行分类的问题,提出了一种基于函数正交基展开的过程支持向量机.该模型的输入为时变函数,输出为模式类别.在输入函数空间中选择一组适当的正交函数基,将输入函数在该组函数基下进行有限项展开,把展开式系数作为核函数的输入.由于时变函数在基函数映射下与展开式系数一一对应,从而可利用SVM的变换机制实现动态模式分类.给出了基于SMO的求解算法,实验结果验证了模型和算法的有效性.     

黄金分割法结合UTD计算机载天线方向图

机载天线辐射特性的研究,对机载天线的一体化设计、布局以及机载电子系统的电磁兼容性(EMC)具有非常重要的意义.应用一致性几何绕射理论(UTD)分析机载天线的辐射特性,利用黄金分割法(0.618法)优化二阶射线寻迹,并考虑了遮挡的判断问题,提高了运算的速度和精度.以机载单极子天线为例.给出了机载天线远场方向图的仿真结果.结果比较令人满意,证明了方法是正确的,并对机载天线耦合分析有一定参考意义.     

演示文稿中填充效果在VB编程中的应用

利用Visual Basic1来调用PowerPoint演示文稿中的填充效果的对象,通过对填充效果属性的分析,找到了各个属性的数值,并结合Access数据库,编写了一个"填充效果"对话框,实现了"填充"效果的设置功能。     

等腰三角形的模式识别

本文应用语言模式识别的方法,对三角形语言识别模式中的等腰三角形的语言识别模型进行识别。取三角形的三条边作为特征因子集,主要根据三角形的特点建立隶属函数并给出建立隶属函数的理论依据。     

Unsupervised and simultaneous training of multiple object detectors from unlabeled surveillance video

Object detection is an essential component in automated vision-based surveillance systems. In general, object detectors are constructed using training examples obtained from large annotated data sets. The inevitable limitations of typical training data sets make such supervised methods unsuitable for building generic surveillance systems applicable to a wide variety of scenes and camera setups. In our previous work we proposed an unsupervised method for learning and detecting the dominant object class in a general dynamic scene observed by a static camera. In this paper, we investigate the possibilities to expand the applicability of this method to the problem of multiple dominant object classes. We propose an idea on how to approach this expansion, and perform an evaluation of this idea using two representative surveillance video sequences.     

Automatic recognition of handwritten medical forms for search engines

A new paradigm, which models the relationships between handwriting and topic categories, in the context of medical forms, is presented. The ultimate goals are: (1) a robust method which categorizes medical forms into specified categories, and (2) the use of such information for practical applications such as an improved recognition of medical handwriting or retrieval of medical forms as in a search engine. Medical forms have diverse, complex and large lexicons consisting of English, Medical and Pharmacology corpus. Our technique shows that a few recognized characters, returned by handwriting recognition, can be used to construct a linguistic model capable of representing a medical topic category. This allows (1) a reduced lexicon to be constructed, thereby improving handwriting recognition performance, and (2) PCR (Pre-Hospital Care Report) forms to be tagged with a topic category and subsequently searched by information retrieval systems. We present an improvement of over 7% in raw recognition rate and a mean average precision of 0.28 over a set of 1,175 queries on a data set of unconstrained handwritten medical forms filled in emergency environments. This work was supported by the National Science Foundation.