英文资料翻译
Image processing is not a one step process．We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene．In this way a hierarchical processing scheme is built up as sketched in Fig． The figure gives an overview of the different phases of image processing.
Image processing begins with the capture of an image with a suitable，not necessarily optical，acquisition system．In a technical or scientific application，we may choose to select an appropriate imaging system．Furthermore，we can set up the illumination system，choose the best wavelength range，and select other options to capture the object feature of interest in the best way in an image．Once the image is sensed，it must be brought into a form that can be treated with digital computers.This process is called digitization.
With the problems of traffic are more and more serious. Thus Intelligent Transport System (ITS) comes out. The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. The image imputed to the computer is disposed and analyzed in order to localization the position and recognition the characters on the license plate express these characters in text string form The license plate recognition system (LPSR) has important application in ITS. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location. In this paper, several of methods in image manipulation are compared and analyzed, then come out the resolutions for localization of the car plate. The experiences show that the good result has been got with these methods. The methods based on edge map and frequency analysis is used in the process of the localization of the license plate, that is to say, extracting the characteristics of the license plate in the car images after being checked up for
the edge, and then analyzing and processing until the probably area of license plate is extracted.
The automated license plate location is a part of the image processing ,it’s also an important part in the intelligent traffic system．It is the key step in the Vehicle License Plate Recognition(LPR).A method for the recognition of images of different backgrounds and different illuminations is proposed in the paper.the upper and lower borders are determined through the gray variation regulation of the character distribution.The left and right borders are determined through the black-white variation of the pixels in every row.
The first steps of digital processing may include a number of different operations and are known as image processing．If the sensor has nonlinear characteristics, these need to be corrected．Likewise，brightness and contrast of the image may require improvement．Commonly，too，coordinate transformations are needed to restore geometrical distortions introduced during image formation．Radiometric and geometric corrections are elementary pixel processing operations．
It may be necessary to correct known disturbances in the image，for instance caused by a defocused optics，motion blur，errors in the sensor，or errors in the transmission of image signals．We also deal with reconstruction techniques which are required with many indirect imaging techniques such as tomography that deliver no direct image.
A whole chain of processing steps is necessary to analyze and identify objects．First，adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background．Essentially，from an image（or several images），one or more feature images are extracted．The basic tools for this task are averaging and edge detection and the analysis of simple neighborhoods and complex patterns known as texture in image processing．An important feature of an object is also its motion．Techniques to detect and determine motion are necessary．Then the object has to be separated from the background．This means that regions of constant features and discontinuities must be identified．This process leads to a
label image．Now that we know the exact geometrical shape of the object，we can extract further information such as the mean gray value，the area，perimeter，and other parameters for the form of the object[3]．These parameters can be used to classify objects．This is an important step in many applications of image processing，as the following examples show：In a satellite image showing an agricultural area，we would like to distinguish fields with different fruits and obtain parameters to estimate their ripeness or to detect damage by parasites．
There are many medical applications where the essential problem is to detect pathologi-al changes．A classic example is the analysis of aberrations in chromosomes．Character recognition in printed and handwritten text is another example which has been studied since image processing began and still poses significant difficulties．
You hopefully do more，namely try to understand the meaning of what you are reading．This is also the final step of image processing，where one aims to understand the observed scene．We perform this task more or less unconsciously whenever we use our visual system．We recognize people，we can easily distinguish between the image of a scientific lab and that of a living room，and we watch the traffic to cross a street safely．We all do this without knowing how the visual system works．For some times now，image processing and computer-graphics have been treated as two different areas．Knowledge in both areas has increased considerably and more complex problems can now be treated．Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes，while image processing is trying to reconstruct one from an image actually taken with a camera．In this sense，image processing performs the inverse procedure to that of computer graphics．We start with knowledge of the shape and features of an object—at the bottom of Fig. and work upwards until we get a two-dimensional image．To handle image processing or computer graphics，we basically have to work from the same knowledge．We need to know the interaction between illumination and objects，how a three-dimensional scene is projected onto an image plane，etc．
There are still quite a few differences between an image processing and a graphics workstation．But we can envisage that，when the similarities and interrelations between computergraphics and image processing are better understood and the proper hardware is developed，we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks[5]．The advent of multimedia，i. e. ，the integration of text，images，sound，and movies，will further accelerate the unification of computer graphics and image processing.
In January 1980 Scientific American published a remarkable image called Plume2，the second of eight volcanic eruptions detected on the Jovian moon by the spacecraft Voyager 1 on 5 March 1979．The picture was a landmark image in interplanetary exploration—the first time an erupting volcano had been seen in space．It was also a triumph for image processing．
Satellite imagery and images from interplanetary explorers have until fairly recently been the major users of image processing techniques，where a computer image is numerically manipulated to produce some desired effect-such as making a particular aspect or feature in the image more visible.
Image processing has its roots in photo reconnaissance in the Second World War where processing operations were optical and interpretation operations were performed by humans who undertook such tasks as quantifying the effect of bombing raids．With the advent of satellite imagery in the late 1960s，much computer-based work began and the color composite satellite images，sometimes startlingly beautiful, have become part of our visual culture and the perception of our planet．
Like computer graphics，it was until recently confined to research laboratories which could afford the expensive image processing computers that could cope with the substantial processing overheads required to process large numbers of high-resolution images．With the advent of cheap powerful computers and image collection devices like digital cameras and scanners，we have seen a migration of image processing techniques into the public domain．Classical image processing techniques are routinely employed by
graphic designers to manipulate photographic and generated imagery，either to correct defects，change color and so on or creatively to transform the entire look of an image by subjecting it to some operation such as edge enhancement.
A recent mainstream application of image processing is the compression of images—either for transmission across the Internet or the compression of moving video images in video telephony and video conferencing．Video telephony is one of the current crossover areas that employ both computer graphics and classical image processing techniques to try to achieve very high compression rates．All this is part of an inexorable trend towards the digital representation of images．Indeed that most powerful image form of the twentieth century—the TV image—is also about to be taken into the digital domain．
Image processing is characterized by a large number of algorithms that are specific solutions to specific problems．Some are mathematical or context-independent operations that are applied to each and every pixel．For example，we can use Fourier transforms to perform image filtering operations．Others are“algorithmic”—we may use a complicated recursive strategy to find those pixels that constitute the edges in an image．
Image processing operations often form part of a computer vision system．The input image may be filtered to highlight or reveal edges prior to a shape detection usually known as low-level operations．In computer graphics filtering operations are used extensively to avoid abasing or sampling artifacts．
中文翻译
图像处理不是一步就能完成的过程。可将它分成诸多步骤，必须一个接一个地执行这些步骤，直到从被观察的景物中提取出有用的数据。依据这种方法，一个层次化的处理方案，该图给出了图像处理不同阶段的概观。
图像处理首先是以适当的但不一定是光学的采集系统对图像进行采集。在技术或科学应用中，可以选择一个适当的成像系统。此外，可以建立照明系统，选择最佳波长范围，以及选择其他方案以便用最好的方法在图像中获取有用的对象特征。一旦图像被检测到，必须将其变成数字计算机可处理的形式，这个过程称之为数字化。
随着交通问题的日益严重，智能交通系统应运而生。汽车牌照自动识别系统是近几年发展起来的计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。课题的目的是对摄像头获取的汽车图像进行预处理,确定车牌位置,提取车牌上的字符串，并对这些字符进行识别处理,用文本的形式显示出来。车牌自动识别技术在智能交通系统中具有重要的应用价值。在车牌自动识别系统中，首先要将车牌从所获取的图像中分割出来，这是进行车牌字符识别的重要步骤，定位准确与否直接影响车牌识别率。本文在对各种车辆图像处理方法进行分析、比较的基础上,提出了车牌预处理、车牌粗定位和精定位的方法，并且取得了较好的定位结果。车牌定位采取的是边缘检测的频率分析法。从经过边缘提取后的车辆图像中提取车牌特征,进行分析处理,从而初步定出车牌的区域，再利用车牌的先验知识和分布特征对车牌区域二值化图像进行处理，从而得到车牌的精确区域。 汽车牌照的自动定位是图像处理的一种，也是智能交通系统中的重要组成部分之一，是实现车牌识别(LPR)系统的关键。针对不同背景和光照条件下的车辆图像，提出了一种基于灰度图像灰度变化特征进行车牌定位的方法。依据车牌中字符的灰度变化以峰、谷规律分布确定车牌上下边界，对扫描行采用灰度跳变法确定车牌左右边界。
数字化处理的第一步包含了一系列不同的操作并被称之为图像处理。如果传感器具有非线性特性，就必须予以校正，同样，图像的亮度和对比度也需要改善。通常，还需要进行坐标变换以消除在成像时产生的几何畸变。辐射度校正和几何校正是最基本的像素处理操作。
在图像中，对已知的干扰进行校正也是不可少的，比如由于光学聚焦不准，运动模糊，传感器误差以及图像信号传输误差所引起的干扰。在此还要涉及图像重构技术，它需要许多间接的成像技术，比如不直接提供图像的X射线断层技术等。
一套完整的处理步骤对于物体的分析和识别是必不可少的。首先，应该采用适当的过滤技术以便从其他物体和背景中将所感兴趣的物体区分出来。实质上就是从一幅图像（或者数幅图像）中抽取出一幅或几幅特征图像。要完成这个任务最基本的工具就是图像处理中所使用的求均值和边缘检测、简单的相邻像素分析，以及复杂的被称为材质描述的模式分析。物体的一个重要特性就是它的运动性。检测和确定物体运动性的技术是必不可少的。随后，该物体必须从背景中分离出来，这就意味着具有同样特性和不同特性的区域必须被识别出来。这个过程产生出标志图像。既然已经知道了物体精确的几何形状，就可以抽取诸如平均灰度值、区域、边界以及形成物体的其他参数等更多的信息。这些参数可用来对物体进行分类，这是许多图像处理应用中至关重要的一步，比如下面一些应用：在一个显示农业地区的卫星图像中，想要区别出不同的果树，并获取参数以估算出成熟情况并监测害虫情况；
在许多的医学应用中，最基本的问题是检查病理变化，最典型的应用就是染色体畸变分析；印刷体和手写体识别是另一个例子，图像处理一出现，人们就开始对它进行着研究，现在依然困难重重。
人们希望能了解得更多一些，也就是试图理解所读到的内容。这也是图像处理的最后一个步骤，即理解所观察到的景象。当我们使用视觉系统时，实际上已或多或少无意识地在执行这个任务。我们能识别不同的人，可以很轻易地区分出实验室和起居室，可以观察车流以便安全地穿行马路。我们完成这样的任务而并不了解视觉系统工作的奥秘。
长久以来，图像处理和计算机图形学被看做两个不同的领域。现在，人们在这两个领域中的知识都有了极大的提高，并可以解决许多复杂的问题。计算机图形学正在努力使三维景物的计算机图像达到照片级效果。而图像处理则试图对用照相机实际拍摄的图像进行重构。从这个意义上讲，图像处理完成的是与计算机图形技术相反的过程。但从有关物体的形状和特性知识开始，向上直到获得一个二维图像要运用图像处理和计算机图形技术，所用到
的基本知识都是一样的。我们需要了解物体和照明之间的相互关系，三维景物是如何投影到图像平面上的等有关知识。
图像处理和计算机图形工作站之间仍然有一些不同之处。但我们应该看到，一旦较好地理解了计算机图形技术和图像处理之间的相似性和相互关系，并开发出了适当的硬件系统，一些既可处理计算机图形，又可完成图像处理任务的通用工作站就会出现。多媒体的出现，即文字、图像、声音和电影的综合，将进一步加速计算机图形学和图像处理的统一。
1980年元月《科学美国人》发表了一幅被称之为“Plume 2”的著名图像，它是1979年3月5日通过宇宙飞船旅行者1号在木星的卫星上探测到的8次火山爆发中的第二次。这幅图像在星际探险图像中是一个里程碑，人们第一次在宇宙中看到了正在爆发的火山。它也是图像处理领域的一次伟大胜利。
卫星图像以及宇宙探测器所获取的图像直到近年来才大量应用图像处理技术。在这些技术中，对计算机图像进行数字化处理以得到想要获得的效果，比如使图像的某一部分或某一特性更加明显。
图像处理源自于二战中的摄影侦察。当时，处理操作是通过光学方法来完成的，判读工作则是由专门精于此道并能确定炸弹袭击结果的人员来做。随着20世纪60年代后期卫星图像的出现，更多基于计算机的工作便开展起来彩色合成的卫星图像，有时的确漂亮得让人吃惊，它们已经成为人类视觉文化和对我们这个行星进行认知的一个组成部分。
正如计算机图形学一样，直到近几年，图像处理仍局限在一些实验室里使用，只有这些地方才能提供昂贵的图像处理计算机来满足处理大量高分辨率图像的需要。随着价格低廉的高性能计算机和诸如数码相机及扫描仪这样的图像采集设备的出现，我们已经看到图像处理技术在向公众领域转移。经典的图像处理技术很平常地被图像设计人员用来处理图片和生成图像，比如修复缺陷，改变色彩等或者通过图像边缘增强这样的处理来改变整个图片外观。
目前图像处理的主流应用是图像的压缩，即通过互联网进行传递或在可视电话和视频会议中进行移动视频图像的压缩。可视电话是当今结合计算机图像和传统图像处理技术，以期产生很高压缩比的交叉领域之一。所有这一
切都是图像的数字表达这一不可抗拒的发展趋势的组成部分。事实上，20世纪最强大的图像形式——电视图像，也将不可避免地融入数字领域。
图像处理的特点是针对不同问题有大量不同的算法。有一些是应用于每一个像素的、数学的或不依赖上下文的运算，比如，可以使用傅里叶变换来完成图像滤波操作；还有一些则是算法上的一一可以在图像中使用复杂的递归策略找出构成边缘的那些像素。
图像处理操作通常形成计算机视觉系统的一部分。比如，在形状检测操作中输入图像可过滤成高光或显示图像边缘。在计算机视觉系统中．这些处理通常认为是低级操作在计算机图形技术中，过滤操作广泛地用于防止图像毛边或采样失真。