论文翻译-林本存

题目： 车牌识别及MATLAB的介绍 专业： 电子工程 指导教师： 章学静 陈竞圆 学院： 电子系 学 号： 2009080403104 班级： 200908040301 姓 名： 林本存

LPR and MATLAB software is introduced

Automatic license plate recognition (LPR) has been a practical technique in the past decades. Numerous applications, such as automatic toll collection, criminal pursuit and traffic law enforcement , have been benefited from it . Although some novel techniques, for example RFID (radio frequency identification), WSN (wireless sensor network), etc., have been proposed for car ID identification, LPR on image data is still an indispensable technique in current intelligent transportation systems for its convenience and low cost. LPR is generally divided into three steps: license plate detection, character segmentation and character recognition. The detection step roughly classifies LP and non-LP regions, the segmentation step separates the symbols/characters from each other in one LP so that only accurate outline of each image block of characters is left for the recognition, and the recognition step finally converts greylevel image block into characters/symbols by predefined recognition models. Although LPR technique has a long research history, it is still driven forward by various arising demands, the most frequent one of which is the variation of LP styles, for example:

（1） Appearance variation caused by the change of image capturing conditions.

（2）Style variation from one nation to another.

（3）Style variation when the government releases new LP format. We summed them up into four factors, namely rotation angle, line number, character type and format, after comprehensive analyses of multi-style LP characteristics on real data. Generally speaking, any change of the above four factors can result in the change of LP style or appearance and then affect the detection, segmentation or recognition

algorithms. If one LP has a large rotation angle, the segmentation and recognition algorithms for horizontal LP may not work. If there are more than one character lines in one LP, additional line separation algorithm is needed before a segmentation process. With the variation of character types when we apply the method from one nation to another, the ability to re-define the recognition models is needed. What is more, the change of LP styles requires the method to adjust by itself so that the segmented and recognized character candidates can match best with an LP format.

Several methods have been proposed for multi-national LPs or multiformat LPs in the past years while few of them comprehensively address the style adaptation problem in terms of the abovementioned factors. Some of them only claim the ability of processing multinational LPs by redefining the detection and segmentation rules or recognition models.

In this paper, we propose a configurable LPR method which is adaptable from one style to another, particularly from one nation to another, by defining the four factors as parameters. Users can constrain the scope of a parameter and at the same time the method will adjust itself so that the recognition can be faster and more accurate. Similar to existing LPR techniques, we also provide details of detection, segmentation and recognition algorithms. The difference is that we emphasize on the configurable framework for LPR and the extensibility of the proposed method for multistyle LPs instead of the performance of each algorithm.

In allusion to the deficiency of the single model location,a new positioning method concerning the license plate，which is based on multi-color modeIs,has been presented in this paper．Firstly,the input image of RGB is transformed into HSV and Y1Q color spaces，from which comprehensive information will be obtained．Further work can be done：by iIltegrating this information,image segmentation will he able to move out large numbers of disturbing background message,leaving those smaller areas(1icense plate itself is included)whose colors are similar to it，pavm the way for license plate location-After that，license plate can be correctly located through the \"two-step-location\"method．In the first

step,the area to he researched will he reduced greatly by the help of section thinking which makes coarse positioning come true．In the second step，the rough located images can be divided into two situations：those in which the color of the target areas of a license plate is quitc different from the non-target area and those in which the color ofthe target area is alike to the non-target． In order to locate the ficense plate precisely,the method of color segmentation and that ofedge detection based on Log operator are adopted respectively．

With the high-speed development of our national economy．there are more and more construct in the domestic expressway，urban road，and parking area，etc．The requisition on the traffic control，safety management improves day and day．The intelligent traffic system (ITS)has already become the main direction of present traffic administration development，and vehicle license plate recognition(LPR)system as core of ，plays a very important role．It occupies important role in the project management of expressway，urban road and parking area，etc．Its wide application will contribute to the process of the traffic management automation of our country．

Being a special computer vision system in the real-time case．the LPR system mainly includes the subsystem of license plate detection and character segmentation and recognition． The LPR system involves numerous discipline domains．such as Pattern Recognition and Artificial Intelligence，Computer Vision，Digital Image Processing etc．The detection of license plates is the key of LPR system．．Because of the complex of image background，the uncertainty of plate position and image quality,the location of plates is not satisfied． Therefore，the study on the algorithm of license plate location is always the hotspot problem．

we have summarized the latest research achievements and development of license plate location and segmentation．This thesis emphasize at the method of cast shadow and the filter method of color．Several characteristic of frame help the correcting of the plate tilting to one side．Its number character iS used to precise the partition of character list district．The experimental results show that this algorithm iS efncient in locating the licenses while not sensitive to unconstrained

illumination conditions and irregular background． Otherwise we do research for character segmentation．

As the world Matlab most widely applied mathematics software has very strong numerical calculation and analysis of data processing, system analysis, the graphic display even symbols of function and the computation of rich toolbox.

License plate location on the need to use the matlab software. MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and Fortran

You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas.

MATLAB provides a number of features for documenting and sharing your work. You can integrate your MATLAB code with other languages and applications, and distribute your MATLAB algorithms and applications.

Key Features：

High-level language for technical computing.

Development environment for managing code, files, and data .

Interactive tools for iterative exploration, design, and problem solving.

Mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, and numerical integration.

2-D and 3-D graphics functions for visualizing data. Tools for building custom graphical user interfaces.

Functions for integrating MATLAB based algorithms with external applications and languages, such as C, C++, Fortran, Java, COM, and Microsoft Excel.

MATLAB provides a high-level language and development tools that let you quickly develop and analyze your algorithms and

applications.

The MATLAB language supports the vector and matrix operations that are fundamental to engineering and scientific problems. It enables fast development and execution.

With the MATLAB language, you can program and develop algorithms faster than with traditional languages because you do not need to perform low-level administrative tasks, such as declaring variables, specifying data types, and allocating memory. In many cases, MATLAB eliminates the need for ‘for’ loops. As a result, one line of MATLAB code can often replace several lines of C or C++ code.

At the same time, MATLAB provides all the features of a traditional programming language, including arithmetic operators, flow control, data structures, data types, object-oriented programming (OOP), and debugging features.

MATLAB lets you execute commands or groups of commands one at a time, without compiling and linking, enabling you to quickly iterate to the optimal solution.

Visualizing Data：

All the graphics features that are required to visualize engineering and scientific data are available in MATLAB. These include 2-D and 3-D plotting functions, 3-D volume visualization functions, tools for interactively creating plots, and the ability to export results to all popular graphics formats. You can customize plots by adding multiple axes; changing line colors and markers; adding annotation, LaTEX equations, and legends; and drawing shapes.

MATLAB contains mathematical, statistical, and engineering functions to support all common engineering and science operations. These functions, developed by experts in mathematics, are the foundation of the MATLAB language. The core math functions use the LAPACK and BLAS linear algebra subroutine libraries and the FFTW Discrete Fourier Transform library. Because these processor-dependent libraries are optimized to the different platforms that MATLAB supports, they execute faster than the equivalent C or C++ code.

MATLAB provides the following types of functions for performing

mathematical operations and analyzing data:

Matrix manipulation and linear algebra . Polynomials and interpolation . Fourier analysis and filtering. Data analysis and statistics .

Optimization and numerical integration . Ordinary differential equations (ODEs) . Partial differential equations (PDEs). Sparse matrix operations.

MATLAB can perform arithmetic on a wide range of data types, including doubles, singles, and integers.

Add-on toolboxes (available separately) provide specialized mathematical computing functions for areas including signal processing, optimization, statistics, symbolic math, partial differential equation solving, and curve fitting.

车牌识别及MATLAB软件介绍

车牌自动识别（车牌识别）在过去几十年一直是很实用的技术。有众多的应用，如自动收费，刑事追求和交通执法人员，已从中受益。虽然一些新技术，例如射频识别 ，无线传感器网络（无线传感器网络）等，已经提出了汽车编号识别，车牌识别图像数据因为其便利性和低成本成为目前智能交通系统中一个不可缺少的技术。车牌识别一般分为三个步骤：车牌检测，字符分割和字符识别。检测步骤大致分为车牌和非车牌地区，分割步骤，分隔符号/字符彼此在同一个车牌，以便在识别时字符块从准确轮廓的左边开始。

车牌识别的最后一步转换灰度图像块成字符/符号识别的预定义模式。虽然车牌识别技术的研究具有悠久的历史，但它仍然被各种需求所驱动，最常见的是其中之一的车牌风格的变化，例如：

（1）外观变化引起的图像采集条件变化。 （2）风格的变化从一个地区到另一个地区。 （3）当发布新的车牌格式时引起的变化。

我们概括起来为四个因素，即转角，行数，字符类型和格式，全面分析后，得到多样式的特点车牌实时数据。一般而言，任何改变上述四个因素可能会导致改变的车牌样式或外观，然后影响到检测，分割和识别算法。如果其中一个车牌号有一个大旋转角的分割与识别算

法的横向车牌可能无法正常工作。如果有多于一个字符线在一个车牌，附加行分离算法需要在分割进程。随着变化的性质类型，当我们的方法适用于从一国到另一国，有能力重新认识模型是必要的。更重要的是，这一变化的车牌样式需要方法来调整车牌本身，使候选的分割和识别好的字符可以匹配车牌的格式。

在过去的几年里，已经有很多种方法提出因为多国车牌或者车牌的多格式，但是上述因素很少能全面解决在风格适应方面的问题，其中一些只要求有能力处理多国车牌的重新检测和分割或识别模式。 在本文中，我们提出了一种可配置的车牌识别方法是适应从一个到另一个风格，尤其是从一国到另一国，所确定的4个因素作为参数。用户可以范围的参数，并在同一时间将调整的方法，使自己的认识可以更快，更准确。与现有的车牌识别技术，我们还提供详细的检测，分割与识别算法。所不同的是，我们强调的配置框架，牌照识别和可扩展性提出的方法多式车牌，而不是每一个算法的性能.

定位方面，针对单一颜色模型分割定位的不足，本文提出了一种基于多颜色模型的车牌定位方法。把RGB彩色车辆图像转化到HSV和YIQ两个颜色空间中，综合这两个颜色空间的信息进行颜色分割能够去除大量的背景干扰信息，只剩下包括车牌在内的较少的与车牌颜色相近的一些区域，更有利于后面的车牌定位。对经过颜色分割后的图像采用两步定位的方法正确定位出车牌。第一步结合分块的思想实现车牌的粗定位大大缩小车牌的搜索区域。第二步，针对车牌目标区域和非目标区域颜色差别较大和相近的粗定位图像分别采用二次颜色分割和基于Log算子进行边缘检测的方法实现车牌的定位，正确定位出车牌。

随着我国国民经济的高速发展，国内高速公路、城市道路、停车场建设越来越多，对交通控制、安全管理的要求也同益提高，智能交通系统(ITS)己成为当前交通管理发展的重要方向，而车辆牌照识别(LPR)系统作为智能交通系统的一部分起着举足轻重的作用，它在高速公路、城市道路和停车场等项目管理中占有无可替代的重要地位，它的广泛应用必将有助于我国交通管理自动化的进程。

车辆牌照识别(LPR)作为一个综合的实时计算机视觉系统主要包括牌照定位、字符分割和字符识别三大部分。它的研究主要涉及到了模式识别、人工智能、计算机视觉、数字图像处理等众多学科领域。车牌的定位与字符分割更是该系统的关键之一，由于图像场景的复杂

性以及车牌位置和图像质量的不可预知性，牌照识别系统一直都未做到令人满意，所以牌照的定位分割算法一直是该领域的研究热点。 通过对大量资料的搜集、整理，总结了近年来国内外在车牌定位分割领域的最新研究成果和最新进展，对车牌区域的固有特征和目前的车牌定位分割技术进行了系统的研究和探讨。着重研究了投影法定位与颜色法过滤相结合的车牌定位方法，实验结果表明该定位方法能够比较快速、准确、鲁棒地定位分割出车辆牌照。在此基础上利用上下边框的几何特征实现了车牌倾斜校正、利用定位过程中得到的投影信息和像素颜色信息实现了边框去除，确保了单个字符切分顺利进行。本文还对车牌图像的字符分割进行了研究，采用一种基于自适应阈值选取的垂直投影法实现了单个字符切分，较好解决了字符粘连、断裂和过切分等问题对字符分割效果的影响。

对车牌定位需要运用到matlab软件。Matlab作为当今世界上应用最为广泛的数学软件，具有非常强大的数值计算、数据分析处理、系统分析、图形显示甚至符号运算的功能和丰富的工具箱。MATLAB 是一种用于算法开发、数据可视化、数据分析以及数值计算的高级技术计算语言和交互式环境。使用 MATLAB，您可以较使用传统的编程语言（如 C、C++ 和 Fortran）更快地解决技术计算问题. MATLAB 的应用范围非常广，包括信号和图像处理、通讯、控制系统设计、测试和测量、财务建模和分析以及计算生物学等众多应用领域。附加的工具箱（单独提供的专用 MATLAB 函数集）扩展了 MATLAB 环境，以解决这些应用领域内特定类型的问题。

MATLAB 提供了很多用于记录和分享工作成果的功能。可以将您的 MATLAB 代码与其他语言和应用程序集成，来分发您的 MATLAB 算法和应用。

主要功能

此高级语言可用于技术计算

此开发环境可对代码、文件和数据进行管理

交互式工具可以按迭代的方式探查、设计及求解问题

数学函数可用于线性代数、统计、傅立叶分析、筛选、优化以及数值积分等

二维和三维图形函数可用于可视化数据 各种工具可用于构建自定义的图形用户界面

各种函数可将基于 MATLAB 的算法与外部应用程序和语言

（如 C、C++、Fortran、Java、COM 以及 Microsoft Excel）集成

MATLAB 提供了一种高级语言和开发工具，使您可以迅速地开发并分析算法和应用程序。

MATLAB 语言支持向量和矩阵运算，这些运算是工程和科学问题的基础。 这样使得开发和运行的速度非常快。

使用 MATLAB 语言，编程和开发算法的速度较使用传统语言大大提高，这是因为 无须执行诸如声明变量、指定数据类型以及分配内存等低级管理任务。 在很多情况下，MATLAB 无须使用 \"for\" 循环。因此，一行 MATLAB 代码经常等效于几行 C 或 C++ 代码。 同时，MATLAB 还提供了传统编程语言的所有功能，包括算法运算符、 流控制、数据结构、数据类型、面向对象编程 (OOP) 以及调试功能。

利用 MATLAB，无须执行编译和链接即可一次执行一个或一组命令，这样就可以迅速迭代到最佳解决方案实现数据可视化

MATLAB 中提供了将工程和科学数据可视化所需的全部图形功能。这些功能包括二维和三维绘图函数、三维卷可视化函数、用于交互式创建图形的工具以及将结果输出为各种常用图形格式的功能。可以通过添加多个坐标轴、更改线的颜色和标记、添加批注、LaTEX 方程和图例以及绘制形状，对图形进行自定义。

MATLAB 包含了各种数学、统计及工程函数，支持所有常见的工程和科算。 这些由数学方面的专家开发的函数是 MATLAB 语言的基础。这些核心的数学函数使用 LAPACK 和 BLAS 线性代数子例程库和 FFTW 离散傅立叶变换库。由于这些与处理器相关的库已针对 MATLAB 支持的各种平台进行了优化，因此其执行速度比等效的 C 或 C++ 代码的执行速度要快。

MATLAB 提供了以下类型的函数，用于执行数算和数据分析：

矩阵操作和线性代数 多项式和内插

傅立叶分析和筛选 数据分析和统计 优化和数值积分 常微分方程 (ODE) 偏微分方程 (PDE)

稀疏矩阵运算

MATLAB 可对包括双精度浮点数、单精度浮点数和整型在内的多种数据类型进行运算。

附加的工具箱（单独提供）提供了专门的数学计算函数，用于包括信号处理、优化、统计、符号数学、偏微分方程求解以及曲线拟合在内的各个领域。