数字图像处理,冈萨雷斯,课件英文版Chapter09.
数字图像处理
数字图像处理(MATLAB版) 实验指导书 (试用版) 本实验指导书配合教材和课堂笔记中的例题使用 姚天曙编写 安徽农业大学工学院 2009年4月试行
目录 实验一、数字图像获取和格式转换 2 实验二、图像亮度变换和空间滤波 6 实验三、频域处理7 实验四、图像复原9 实验五、彩色图像处理10 实验六、图像压缩11 实验七、图像分割13 教材与参考文献14
《数字图像处理》实验指导书 实验一、数字图像获取和格式转换 一、实验目的 1掌握使用扫描仪、数码相机、数码摄像级机、电脑摄像头等数字化设备以及计算机获取数字图像的方法； 2修改图像的存储格式；并比较不同压缩格式图像的数据量的大小。 二、实验原理 数字图像获取设备的主要性能指标有x、y方向的分辨率、色彩分辨率（色彩位数）、扫描幅面和接口方式等。各类设备都标明了它的光学分辨率和最大分辨率。分辨率的单位是dpi，dpi是英文Dot Per Inch的缩写，意思是每英寸的像素点数。 扫描仪扫描图像的步骤是:首先将欲扫描的原稿正面朝下铺在扫描仪的玻璃板上，原稿可以是文字稿件或者图纸照片；然后启动扫描仪驱动程序后，安装在扫描仪内部的可移动光源开始扫描原稿。为了均匀照亮稿件，扫描仪光源为长条形，并沿y方向扫过整个原稿；照射到原稿上的光线经反射后穿过一个很窄的缝隙，形成沿x方向的光带，又经过一组反光镜，由光学透镜聚焦并进入分光镜，经过棱镜和红绿蓝三色滤色镜得到的RGB三条彩色光带分别照到各自的CCD上，CCD将RGB光带转变为模拟电子信号，此信号又被A/D变换器转变为数字电子信号。至此，反映原稿图像的光信号转变为计算机能够接受的二进制数字电子信号，最后通过串行或者并行等接口送至计算机。扫描仪每扫一行就得到原稿x方向一行的图像信息，随着沿y方向的移动，在计算机内部逐步形成原稿的全图。扫描仪工作原理见图1.1。
数字图像处理英文原版及翻译
Digital Image Processing and Edge Detection Digital Image Processing Interest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception. An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pixels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a
计算机图形_Digital Image Processing, 2nd ed(数字图像处理(第2版))
Digital Image Processing, 2nd ed(数字图像处理(第2 版)) 数据摘要： DIGITAL IMAGE PROCESSING has been the world-wide leading textbook in its field for more than 30 years. As the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992 edition by Gonzalez and Woods, the present edition was prepared with students and instructors in mind. The material is timely, highly readable, and illustrated with numerous examples of practical significance. All mainstream areas of image processing are covered, including a totally revised introduction and discussion of image fundamentals, image enhancement in the spatial and frequency domains, restoration, color image processing, wavelets, image compression, morphology, segmentation, and image description. Coverage concludes with a discussion on the fundamentals of object recognition. Although the book is completely self-contained, this companion web site provides additional support in the form of review material, answers to selected problems, laboratory project suggestions, and a score of other features. A supplementary instructor's manual is available to instructors who have adopted the book for classroom use.
图像处理英文翻译
数字图像处理英文翻译 （Matlab帮助信息简介） xxxxxxxxx xxx Introduction MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation. Using the MATLAB product, you can solve technical computing problems 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. The MATLAB system consists of these main parts: Desktop Tools and Development Environment This part of MATLAB is the set of tools and facilities that help you use and become more productive with MATLAB functions and files. Many of these tools are graphical user interfaces. It includes: the
数字图像处理 外文翻译 外文文献 英文文献 数字图像处理
Digital Image Processing 1 Introduction Many operators have been proposed for presenting a connected component n a digital image by a reduced amount of data or simplied shape. In general we have to state that the development, choice and modi_cation of such algorithms in practical applications are domain and task dependent, and there is no \best method". However, it is interesting to note that there are several equivalences between published methods and notions, and characterizing such equivalences or di_erences should be useful to categorize the broad diversity of published methods for skeletonization. Discussing equivalences is a main intention of this report. 1.1 Categories of Methods One class of shape reduction operators is based on distance transforms. A distance skeleton is a subset of points of a given component such that every point of this subset represents the center of a maximal disc (labeled with the radius of this disc) contained in the given component. As an example in this _rst class of operators, this report discusses one method for calculating a distance skeleton using the d4 distance function which is appropriate to digitized pictures. A second class of operators produces median or center lines of the digital object in a non-iterative way. Normally such operators locate critical points _rst, and calculate a speci_ed path through the object by connecting these points. The third class of operators is characterized by iterative thinning. Historically, Listing [10] used already in 1862 the term linear skeleton for the result of a continuous deformation of the frontier of a connected subset of a Euclidean space without changing the connectivity of the original set, until only a set of lines and points remains. Many algorithms in image analysis are based on this general concept of thinning. The goal is a calculation of characteristic properties of digital objects which are not related to size or quantity. Methods should be independent from the position of a set in the plane or space, grid resolution (for digitizing this set) or the shape complexity of the given set. In the literature the term \thinning" is not used
外文翻译----数字图像处理与边缘检测
附录1 译文 数字图像处理与边缘检测 数字图像处理 数字图像处理方法的研究源于两个主要应用领域：其一是为了便于人们分析而对图像信息进行改进：其二是为使机器自动理解而对图像数据进行存储、传输及显示。 一幅图像可定义为一个二维函数f(x,y)，这里x和y是空间坐标，而在任何一对空间坐标（x,y）上的幅值f 称为该点图像的强度或灰度。当x,y和幅值f 为有限的、离散的数值时，称该图像为数字图像。数字图像处理是指借用数字计算机处理数字图像，值得提及的是数字图像是由有限的元素组成的，每一个元素都有一个特定的位置和幅值，这些元素称为图像元素、画面元素或像素。像素是广泛用于表示数字图像元素的词汇。 视觉是人类最高级的感知器官，所以，毫无疑问图像在人类感知中扮演着最重要的角色。然而，人类感知只限于电磁波谱的视觉波段，成像机器则可覆盖几乎全部电磁波谱，从伽马射线到无线电波。它们可以对非人类习惯的那些图像源进行加工，这些图像源包括超声波、电子显微镜及计算机产生的图像。因此，数字图像处理涉及各种各样的应用领域。 图像处理涉及的范畴或其他相关领域（例如，图像分析和计算机视觉）的界定在初创人之间并没有一致的看法。有时用处理的输入和输出内容都是图像这一特点来界定图像处理的范围。我们认为这一定义仅是人为界定和限制。例如，在这个定义下，甚至最普通的计算一幅图像灰度平均值的工作都不能算做是图像处理。另一方面，有些领域（如计算机视觉）研究的最高目标是用计算机去模拟人类视觉，包括理解和推理并根据视觉输入采取行动等。这一领域本身是人工智能的分支，其目的是模仿人类智能。人工智能领域处在其发展过程中的初期阶段，它的发展比预期的要慢的多，图像分析（也称为图像理解）领域则处在图像处理和计算机视觉两个学科之间。 从图像处理到计算机视觉这个连续的统一体内并没有明确的界线。然而，在这个连续的统一体中可以考虑三种典型的计算处理（即低级、中级和高级处理）来区分其中的各个学科。 低级处理涉及初级操作，如降低噪声的图像预处理，对比度增强和图像尖
数字图像处理与边缘检测中英文对照外文翻译文献
中英文资料对照外文翻译 Digital Image Processing and Edge Detection Digital Image Processing Interest in digital image processing methods stems from two principal applica- tion areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for au- tonomous machine perception. An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image. Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications. There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields a single number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than
数字图像处理外文翻译参考文献
数字图像处理外文翻译参考文献 (文档含中英文对照即英文原文和中文翻译) 原文： Application Of Digital Image Processing In The Measurement Of Casting Surface Roughness Ahstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parameters and the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.
数字图像处理课件整理
第一章 ?课程性质和任务 通过本课程的学习，系统地了解数字图像的基本概念、数字图像形成的原理，掌握数字图像处理的理论基础和技术方法。着重掌握数字图像的增强、复原、压缩和分割的技术方法，为今后能够从事有关数字图像处理的研究和技术方法应用等工作掌握必备的基础知识。 数字图像处理的概念 1. 什么是图像 ?图像可定义为一个二维函数f (x, y) ?(x,y)——空间坐标 ?幅度值f (x, y)——图像该点的灰度(或强度) ?数字图像：坐标x、y和幅度f(x,y)均是有限的离散数值 ?数字图像中每个由坐标(x,y)指定的点称为像素(pixel)。 ?数字图像可看作是由像素组成的二维矩阵。 灰度图像 ?对于单色即灰度图像而言，每个像素的亮度用一个数值来表示，通常数值范围在0到255之间。 0表示黑、255表示白，而其它表示灰度级别。
2.什么是数字图像处理 数字图像处理就是利用计算机系统对数字图像进行各种目的的处理 3. 数字图像的表示方法 空间上：图像抽样 对连续图像f（x，y）进行数字化 幅度上：灰度级量化 x方向，抽样M行 y方向，每行抽样N点 整个图像共抽样M×N个像素点 一般取M=N=2n=64，128，256，512，1024，2048…… 四、数字图像处理的三个层次 ?从计算机处理的角度可以由低到高将数字图像处理分为三个层次。这三个层次覆盖了图像处理的 所有应用领域 1. 图像处理： 对图像进行各种加工，以改善图像的视觉效果；强调图像之间进行的变换； 图像处理是一个从图像到图像的过程。 2. 图像分析：对图像中感兴趣的目标进行提取和分割，获得目标的客观信息（特点或性质），建立对图像的描述； ?以观察者为中心研究客观世界； ?图像分析是一个从图像到数据的过程。 3. 图像理解：研究图像中各目标的性质和它们之间的相互联系；得出对图像内容含义的理解及原来客观场景的解释； ?以客观世界为中心，借助知识、经验来推理、认识客观世界，属于高层操作（符号运算）。 五、数字图像处理的主要研究内容 1.图像变换 2.图像压缩编码 3.图像的增强和复原 4.图像分割 5.图像描述 6.图像识别
英文翻译
中文翻译 数字图像处理方法的研究 1 绪论 数字图像处理方法的研究源于两个主要应用领域:其一是为了便于人们分析而对图像信息进行改进；其二是为了使机器自动理解而对图像数据进行存储、传输及显示。 1.1 数字图像处理的概念 一幅图像可定义为一个二维函数f(x, y)，这里x和y是空间坐标，而在任何一对空间坐标f(x, y)上的幅值f称为该点图像的强度或灰度。当x，y和幅值f为有限的、离散的数值时，称该点是由有限的元素组成的，没一个元素都有一个特定的位置和幅值，这些元素称为图像元素、画面元素或象素。象素是广泛用于表示数字图像元素的词汇。在第二章，将用更正式的术语研究这些定义。 视觉是人类最高级的感知器官，所以，毫无疑问图像在人类感知中扮演着最重要的角色。然而，人类感知只限于电磁波谱的视觉波段，成像机器则可覆盖几乎全部电磁波谱，从伽马射线到无线电波。它们可以对非人类习惯的那些图像源进行加工，这些图像源包括超声波、电子显微镜及计算机产生的图像。因此，数字图像处理涉及各种各样的应用领域。 图像处理涉及的范畴或其他相关领域(例如，图像分析和计算机视觉)的界定在初创人之间并没有一致的看法。有时用处理的输人和输出内容都是图像这一特点来界定图像处理的范围。我们认为这一定义仅是人为界定和限制。例如，在这个定义下，甚至最普通的计算一幅图像灰度平均值的工作都不能算做是图像处理。另一方面，有些领域(如计算机视觉)研究的最高目标是用计算机去模拟人类视觉，包括理解和推理并根据视觉输人采取行动等。这一领域本身是人工智能的分支，其目的是模仿人类智能。人工智能领域处在其发展过程中的初期阶段，它的发展比预期的要慢得多，图像分析(也称为图像理解)领域则处在图像处理和计算机视觉两个学科之间。 从图像处理到计算机视觉这个连续的统一体内并没有明确的界线。然而，在这个连续的统一体中可以考虑三种典型的计算处理(即低级、中级和高级处理)来区分其中的各个学科。低级处理涉及初级操作，如降低噪声的图像预处理，对比度增强和图像尖锐化。低级处理是以输人、输出都是图像为特点的处理。中级处理涉及分割〔把图像分为不同区域或目标物)以及缩减对目标物的描述，以使其更适合计算机处理及对不同日标的分类(识别)。中级图像处理是以输人为图像，但输出是从这些图像中提取的特征(如边缘、轮廓及不同物体的标识等)为特点的。最后，高级处理涉及在图像分析中被识别物体的总体理解，以及执行与视觉相关的识别函数(处在连续统一体边缘)等。 根据上述讨论，我们看到，图像处理和图像分析两个领域合乎逻辑的重叠区域是图像中特定区域或物体的识别这一领域。这样，在本书中，我们界定数字图
《英美概况》课件介绍.
《英美概况》课件介绍 一、课件简介 《英美概况》课件为单机版课件，主要用于大学英语专业本科生《英美概况》课程的授课使用。课件紧密结合《英美概况》课程教学实践，以授课顺序为纲目，采用交互方式将全部课程内容组织在一起，条理清楚，层次分明，内容翔实，界面友好，既可用于教师的备课、授课活动，也可对学生进行测试，同时还增加了互动学习的扩展功能。本课件也可用于大学非英语专业学生《英美概况》选修课及中学英语教学中英美知识部分的授课。 二、课件特色 1、内容全面 课件包含了大学英语专业《英美概况》课程的主要内容，涉及到英国、美国的地理、历史、政治、经济、教育等方面，可以满足任何层次的《英美概况》课程教学。 2、结构合理 课件结构采取了树状结构，由上及下、由表及里、由面及点，有利于学生系统掌握所学内容。 3、资源丰富 课件资源来自于教材、音像资料、网络及其它资料，表现形势包含文字、图片、视频、音频等，极大丰富了课件的内容。 4、交互性强 课件针对每节课内容给出了复习题，采用交互方式可由教师组织或学生自主进行练习测试。 5、介面清晰、操作简便 课件介面设计较规范，使用者很容易把握，所有链接大都有专门的图标或按钮。 三、制作平台 课件采用了现今较为先进的多课件制作工具Authorware 7.0，同时利用Photoshop,
Premiere、CoolEdit等软件进行图像、视频、音频的制作与采集，综合运用多媒体手段达到教学目的。 四、应用效果 本课件在北京工业大学英语专业2003级、2004级及校选课的《英美概况》课程教学中使用，效果良好，学生的学习积极性有了较大提高，学生普遍反映使用本课件后有助于理解和掌握教学内容，便于记忆，直观性很强。教师在应用本课件组织教学中，也感到备课有条理，上课思路也更清晰，讲课更生动，易于和学生沟通。 五、运行环境 课件打包后为.EXE文件，在目前流行的各种操作系统下均可运行。课件中视频进行了一定的压缩，运行课件前需安装随盘的插件klcodec205f.exe（在先运行目录中），课件显示分辨率为1024×768，由于课件容量较大，推荐硬件配置P4 3.0G、1G内存、128M显存。 附录一: 界面功能介绍
数字图像处理 外文翻译 外文文献 英文文献 数字图像处理
数字图像处理外文翻译外文文献英文文献数字图像处 理 Digital Image Processing 1 Introduction Many operators have been proposed for presenting a connected component n a digital image by a reduced amount of data or simplied shape. In general we have to state that the development, choice and modi_cation of such algorithms in practical applications are domain and task dependent, and there is no \best method". However, it is interesting to note that there are several equivalences between published methods and notions, and characterizing such equivalences or di_erences should be useful to categorize the broad diversity of published methods for skeletonization. Discussing equivalences is a main intention of this report. 1.1 Categories of Methods One class of shape reduction operators is based on distance transforms. A distance skeleton is a subset of points of a given component such that every point of this subset represents the center of a maximal disc (labeled with the radius of this disc) contained in the given component. As an example in this _rst class of operators, this report discusses one method for calculating a distance skeleton using the d4 distance function which is appropriate to digitized pictures. A second class of operators produces median or center lines of the digital
数字图像处理论文中英文对照资料外文翻译文献
中英文对照资料外文翻译文献 原文 To image edge examination algorithm research Abstract ：Digital image processing took a relative quite young discipline, is following the computer technology rapid development, day by day obtains the widespread application.The edge took the image one kind of basic characteristic, in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widespread application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develops the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainly has Robert, Laplacian, Sobel, Canny, operators and so on LOG。 First as a whole introduced digital image processing and the edge detection survey, has enumerated several kind of at present commonly used edge detection technology and the algorithm, and selects two kinds to use Visual the C language programming realization, through withdraws the image result to two algorithms the comparison, the research discusses their good and bad points. Foreword：In image processing, as a basic characteristic, the edge of theimage, which is widely used in the recognition, segmentation,intensification and compress of the image, is often applied tohigh-level domain.There are many kinds of ways to detect the edge. Anyway, there aretwo main techniques: one is classic method based on the gray grade ofevery pixel; the other one is based on wavelet and its multi-scalecharacteristic. The first method, which is got the longest research,get the edge according to the variety of the pixel gray.
外文翻译----数字图像处理方法的研究(中英文)
The research of digital image processing technique 1Introduction Interest in digital image processing methods stems from two principal application areas:improvement of pictorial information for human interpretation;and processing of image data for storage,transmission,and representation for autonomous machine perception.This chapter has several objectives:(1)to define the scope of the field that we call image processing;(2)to give a historical perspective of the origins of this field; (3)to give an idea of the state of the art in image processing by examining some of the principal area in which it is applied;(4)to discuss briefly the principal approaches used in digital image processing;(5)to give an overview of the components contained in a typical,general-purpose image processing system;and(6)to provide direction to the books and other literature where image processing work normally is reporter. 1.1What Is Digital Image Processing? An image may be defined as a two-dimensional function,f(x,y),where x and y are spatial(plane)coordinates,and the amplitude of f at any pair of coordinates(x,y)is called the intensity or gray level of the image at that point.When x,y,and digital image.The field of digital image processing refers to processing digital images by means of a digital computer.Note that a digital image is composed of a finite number of elements,each of which has a particular location and value.These elements are referred to as picture elements,image elements,pels,and pixels.Pixel is the term most widely used to denote the elements of a digital image.We consider these definitions in more formal terms in Chapter2. Vision is the most advanced of our senses,so it is not surprising that images play the single most important role in human perception.However,unlike human who are limited to the visual band of the electromagnetic(EM)spectrum,imaging machines cover almost the entire EM spectrum,ranging from gamma to radio waves.They can operate on images generated by sources that human are not accustomed to associating with image.These include ultrasound,electron microscopy,and computer-generated images.Thus,digital image processing encompasses a wide and varied field of application. There is no general agreement among authors regarding where image processing stops and other related areas,such as image analysis and computer vision,start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images.We believe this to be a limiting and somewhat artificial boundary.For example,under this definition,even the trivial task of computing the average intensity of an image(which yields a single number)would not be considered an image processing operation.On the other hand, there are fields such as computer vision whose ultimate goal is to use computer to