Web-based document image processing

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教育游戏化:将课堂变成一场协同冒险游戏——以Classcraft为例

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“对战”形式完成教学评测。学生按时完成任务可以获得奖励，并用来升级角色的经验值（Experience Points，XP）——这将使其角色提高战斗水平并学习新的技能。如果一个学生违反了课堂纪律，就会失去生命值，甚至最终导致角色在“对战”中失败。如果学生获得经验值点数，对相应角色及其团队都有益处；相反，如果一个学生失去了生命值点数，其团队的其他成员角色也会受到伤害，并且大家必须完成各种额外任务。无论如何，学生们需要共同努力才能使团队获得成功。一般而言，没有学生愿意自己的不当行为损害团队利益，导致他人失败。游戏团队中，学生还可以帮助彼此成长。例如，如果学生的虚拟角色是一名战士，而队友因为上课迟到面临生命值点数降低，则该学生可以通过完成额外的学习任务来挽救队友。学生知道他们在课堂上的行为会影响整个团队的进度、这会激励他们强化课堂上的积极行为和团队合作，提升课堂学习效率。 Classcraft 每个月都会发布新的故事情节和场景供教育工作者选择，帮助提升学生的课堂参与感 [19]。除了在预制故事中添加课程任务外，Classcraft 还允许教师自己编写课程，通过上传不同的学习任务来教授不同的科目。根据在课堂活动中收集的数据，教师还可以查看学生的行为并进行分析。
是以游戏软件为基础的学习，教育游戏（Educational Games）的设
计与开发是当前研究的主流方向。教育游戏模糊了学习与游戏、正式学习与非正式学习的边界 [13]；但是有别于教育游戏的软件性质（见表
1），教育游戏化是一套解决方案，服务于教育情境中的各类问题，
如激发学习者动机和兴趣、引导学习者面对学业失败、激发其学校生
研究表明，随着游戏在当代文化中的地位日益提高，其在教育中能够扮演的角色也越来越多样化。 Classcraft 作为受到游戏启发开发的教育解决方案，它对于学习的积极作用和游戏非常相似。

20秋西南大学[0089]《专业英语》作业辅导资料

0089 20202单项选择题1、When power is removed, information in the semiconductor memory is ( )1.manipulated2.reliable3.remain4.lost2、A computer system can roughly be divided into three components except( )1.hardware2.application software3.system software4.CPU3、A processor is composed of two functional units, they are ( )1.an arithmetric/logic unit and a storage unit2. a control unit and an arithemetric/logic unit3.some registers and arithmetric/logic unit4. a control unit and some registers4、( ) is a storage location inside the CPU1.Memory2.Control3.ALU4. A register5、The basic units of a computer system are as follows( )1.CPU, Memory and disk2.CPU, Memory and I/O System3.CPU ,Input and output unit4.CPU, Memory and ALU6、( ) refers to the process of a two dimentional picture by a digital computer1.Image data file format2.Digital image processing3.Pattern recognition4.Image compression7、CPU is an important part of the computer, and it can interpret and ( ) information.1.processe3.brain4.heart8、Which following is not big 4 tech company?( )1.Ubber2.Facebook3.Apple4.Google9、Machine -language instructions are a series of ( )1.abstract codes2.0s and 1s3.words4.machine codes10、Which of the following is not an applicaton software?1. E. web browserpiler3.word processor4.database program11、Many companies use( ) to train employees.te1.technology2.multimedia applications3.animation4.entertainment12、The highest award of Computer Science is ACM( ) award1.Bill Gates2.Andrew Groves3.Alan Turing4.Steve Jobs13、Multimedia means that ( )1.it can play music2.it can rotate a three-dimensional model3.it can do all above at the same time4.it can show a graph14、When the file is not saved, document in the processing is ( )1.reliable2.lost3.remain4.manipulated15、The founder of tencent is ( )1. C. Robin LI2.Jack lee3.Jack ma4.Pony Ma16、An ISP supplies( ) that you can dial from your computer to log on the internet server.1.Help file2.Private key3.Public key4.Service number17、The windows product line includes ( )1. D. windows me2.above all3.windows xp4.windows 200018、Which following is not Object-oriented language？1.Python2.Asembly language3.Java4.C++19、Static graphics include( )1.Animators2.Pictures3.Movies4.Videos20、The ( ) serves as an interface between hardware and software1.System2.application program3.control unit4.operating system21、With IE and an Internet connection, You can search veiw the information on ( )1.Active Desktop2.Phone Dialer3.Programs4.World wide web22、The name of first electronic computer is ( )1. A. ENIAC2.ERICA3.APPLE4.EIACN23、Please find the item that is not belong to the DBA ( )1. a transaction2. a file system3. a database system4. a database language24、( )program also has potential benefits in parallel processing1.machine2.process-oriented3.object-oriented4.assembly25、The input/output devices are called( )1.Peripherals2.Cache3.Storage4.Memory26、The Internet became a lot easier for public to learn and use because of the common ( )1.topologies2.architecture3.protocolsmands27、Service that Internet can not provide includes ( )1.web surf2.cooking3.email4.web live28、( ) is designed to manage large bodies of information.1. a database language2. a file system3. a transaction4. a database system29、Today, ( ) can give you a music synthesizer, a fax machine, a CD-ROM drive, ect.1.Expansion cards2.Output device3.Joystick4.Input devices30、The end equipment in communication system does not include( )puters2.DCE3.CRTs4.keyboards判断题31、With the development of computer, the physical size of the CPU has often become bigger and1. A.√2. B.×32、The CPU is responsible for performing some arithmetric operations and logic decisions.1. A.√2. B.×33、The chipset consists of two parts: North Bridege and South Bridge1. A.√2. B.×34、O2O Model refers to online to offline1. A.√2. B.×35、an input/output device performs both input and output functions, such as a computer data s drive, memory card and tape drive)1. A.√2. B.×36、The movement of electronic signals between main memory and the ALU as well as the control controlled by the control unit of the CPU1. A.√2. B.×37、The control unit performs all the arithmetric and logical functions1. A.√2. B.×38、The four basic units of simplified computer:the input unit, central processing unit, memor1. A.√2. B.×39、The central processing unit is the heart of the computer systems.1. A.√2. B.×40、The number of IPv4 is unlimited1. A.√2. B.×41、The binary language which they are written in machine instruction is called machine langua1. A.√2. B.×42、ADD AX,BX is an instruction of machine language1. A.√2. B.×43、We can use Email only as a one-to-one platform1. A.√2. B.×44、Registers in the control unit are used to keep track of the overall status of the program1. A.√2. B.×45、The basic resources of a computer system are software and data1. A.√2. B.×46、The CPU compreises the control unit and memory1. A.√2. B.×47、Main storage and auxiliary storage are sometimes called internal memory and external memor1. A.√2. B.×48、an output device provides output from the computer, such as a computer monitor, projector,1. A.√2. B.×49、RAM is normally associated with volatile types of memory (such as DRAM modules), where sto1. A.√2. B.×50、Memory is the heart of computer system1. A.√2. B.×主观题51、A relational database is a digital database based on the of ( ) data参考答案：relational model52、A software system used to maintain relational databases is a ( )参考答案：relational database management system53、CPU chips now contain （）memory—a small amount of fast SRAM参考答案：cache54、The dominant desktop operating system is ( )with a market share of around 82.74%参考答案：Microsoft Windows55、Social media marketing is commercial promotion conducted through ( )websites.参考答案：social media56、In operating systems, ( )is the function responsible for managing the computer's prima 参考答案：memory management57、Please translate the following into English计算机芯片，也称为芯片，集成电路或嵌入了集成电路的半导体材料的小晶圆参考答案：Computer chip, also called chip, integrated circuit or small wafer of semiconductor 58、Please translate the following into Chinese通常，现代计算机由至少一个处理元件组成，通常是以金属氧化物半导体（MOS）微处理器形式组成的中央MOS 半导体）内存芯片。

Computer-Vision计算机视觉英文ppt

At the same time, AI MIT laboratory has attracted many famous scholars from the world to participate in the research of machine vision,which included the theory of machine vision , algorithm and design of system .
Its mainstream research is divided into three stages:
Stage 1: Research on the visual basic method ,which take the model world as the main object;
Stage 2: Research on visual model ,which is based on the computational theory;
the other is to rebuild the three dimensional object according to the two-dimensional projection images .
History of computer vision
1950s: in this period , statistical pattern recognition is most applied in computer vision , it mainly focuse on the analysis and identification of two-dimensional image,such as: optical character recognition, the surface of the workpiece, the analysis and interpretation of the aerial image.

边缘检测中英文翻译

Digital Image Processing and Edge DetectionDigital Image ProcessingInterest 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, 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) spectrum, 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 ultrasound, 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 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 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 originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low, mid, and highlevel processes. Low-level processes involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higherlevel processing involves “making sense” of an ensemble of recognize d objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.” As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some formof organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnetic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other.Fig1Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image.A familiar example of enhancement is when we increase the contrast of an imagebecause “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” en hancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.F ig2Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmi it.Although storagetechnology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the pro cess that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital imageprocessing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge dectection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal b lur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problemof detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking outirrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholding parameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem insegmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.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：其一是改进图像信息以便于人们分析；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。

lbp3500维修手册

Indicates an item requiring care to avoid electric shocks.
Indicates an item requiring care to avoid combustion (fire).
Indicates an item prohibiting disassembly to avoid electric shocks or problems.
1.3 Product Specifications ................................................................................................................................1- 1 1.3.1 Specifications .......................................................................................................................................................... 1- 1
1.4 Name of Parts.............................................................................................................................................1- 3 1.4.1 External View........................................................................................................................................................... 1- 3 1.4.2 Cross Section .......................................................................................................................................................... 1- 4

多媒体及艺术设计专业英语Chapter 3 Web Design

Chapter 3 Web Design本章目标设计：通过本章关于网页设计的学习，能利用已有专业知识理解本章中的英语专业文章，运用构词法与记忆技巧识记本章中的专业词汇，翻译专业词汇、句子和段落，进而为掌握专业英语其它相关专业知识打下基础。

I．能力目标：1．能利用已有专业知识理解英语专业文章；2．能完成关于网页制作相关知识的实训；3．会利用网络查找最新的网页设计技术动态；4．运用翻译技巧进行专业词汇、句子和段落翻译。

II．知识目标：1．了解网页设计的编程语言；2．分析网页设计在艺术设计中的作用；3．掌握本章中的专业词汇；4．掌握翻译技巧。

III．情感目标：1．培养专业英语学习的兴趣；2．形成良好的英语学习方法。

3.1 Internet Service FunctionTask 1: Enumerate the Web browsers we often use.Task 2: Write down what did you do with the Internet in English.Task 3: Talk about how to send out an E-mail.Task 4: Explain how to use FTP in your studying.Task 5: Contrasts FTP with the World Wide Web, then speaks out the difference between them.Once your computer enters into a connection with the Internet, you will find that you have walked into the largest repository of information. The two most popular Web browsers are Microsoft Internet Explorer and Netscape Navigator. A Web browser presents data in multimedia on Web pages that use text, graphics, sound and video.The Web pages are created with a formal language called Hypertext Markup Language (HTML). The term hypertext is used to describe an interlinked system of documents in which a user may jump from one document to another in a nonlinear way. Hyperlink makes the Internet easy to navigate. It is an object (word, phrase, or picture) on a Web page that, when clicked, transfers you to a new Web page. The Web page contains an address location known as Uniform Resource Locator (URL). When hypertext pages are mixed with other media, the result is called hypermedia.The following are the important service functions that Internet provides.1. E-mailThe most widely used tool on the Internet is electronic mail or E-mail. E-mail enables you to send messages to America, Australia and so on, no matter how far between individuals. E-mail messages are generally sent from and received by mail servers—computers that are dedicated to processing and directing E-mail. Once a server has received a message it directs it to the specific computer that the E-mail is addressed to. To send E-mail, the process is reversed. As a very convenient and inexpensive way to transmit messages, E-mail has grammatically affected scientific, personal, and business communications. In some cases, E-mail has replaced the telephone for carrying messages.2. File transferFile Transfer Protocol (FTP) is a method of transferring files from one computer to another over the Internet, even if each computer has a different operating system or storage format. FTP is designed to download files or upload files. The ability to upload and download files on it is one of the most valuable features the Internet has to offer. This is especially helpful for those people who rely on computers for various purposes and who may need software drivers and upgrades immediately. Network administrators can rarely wait even a few days to get the necessary drivers that enable their network servers to function again. The Internet can provide these files immediately by using FTP. FTP is a client-server application just like E-mail and Telnet. It requires server software running on a host that can be accessed by client software.3. The World Wide WebThe World Wide Web (WWW), which Hypertext Transfer Protocol (HTTP) works with, is the fastest growing and most widely-used part of the Internet. It provides access to multipleservices and documents as Gopher does but is more ambitious in its method. A jump to other Internet service can be triggered by a mouse click on a “hot-linked” word, image, or icon on the Web pages. One of the main reasons for the extraordinary growth of the Web is the ease in which it allows access to information. One limitation of HTTP is that you can only use it to download files, and not to upload them.4. TelnetTelnet allows an Internet user to connect to a distance computer and use that computer as if he or she were using it directly. To make a connection with a Telnet client, you must select a connection option: “Host Name” and “Terminal Type”. The host name is the IP address (DNS) of the remote computer to which you connect. The terminal type describes the type of terminal emulation that you want the computer to perform.The Internet has many new technologies, such as global chat, video conferencing, free international phone and more. The Internet becomes more and more popular in society in recent years. So we can say that Internet is your PC’s window to the rest of the world.Key Termshypertext 超文本hyperlink 超链接hypermedia 超媒体client-server 客户-服务器mail server 邮件服务器FTP(File Transfer Protocol) 文件传输协议WWW(World Wide Web) 万维网Telnet 远程登录DNS(Domain Name Server) 域名服务器video conferencing 电视会议HTML(hypertext Markup Language) 超文本链接标示语言URL(Uniform Resource Locator) 统一资源定位符IP(Internet Protocol) 互联网协议，网际协议Vocabularyrepository n.仓库，资源丰富的地方nonlinear adj.非线性的dedicate to 用做…，奉献ambitious adj.雄心的，野心的trigger v.引发，引起，触发extraordinary adj.特别的，非常的terminal n.终端emulation n.竞争，效法TrainingI. Translate the following sentences.1.Once your computer enters into a connection with the Internet, you will find that you have walked into the largest repository of information.2. E-mail messages are generally sent from and received by mail servers—computers that are dedicated to processing and directing E-mail.3. File Transfer Protocol (FTP) is a method of transferring files from one computer to another over the Internet, even if each computer has a different operating system or storage format. FTP is designed to download files or upload files.4.The World Wide Web (WWW), which Hypertext Transfer Protocol (HTTP) works with, is the fastest growing and most widely-used part of the Internet.5. Telnet allows an Internet user to connect to a distance computer and use that computer as if he or she were using it directly. To make a connection with a Telnet client, you must select a connection option: “Host Name” and “Terminal Type”.II. Write T (true) or F (false) for each statement.1. The term hypertext is used to describe an interlinked system of documents in which a user may jump from one document to another in a nonlinear way.2. It is an object (word, phrase, or picture) on a Web page that, when clicked, transfers you toa new Web page.3. E-mail messages are generally sent from and received by mail servers—computers that are dedicated to processing and directing E-mail.4. The World Wide Web (WWW), which Hypertext Transfer Protocol (HTTP) works with, is the fastest growing and most widely-used part of the Internet.III. Fill in the blanks with proper words.The Web pages are created with a formal language called ___ ___, and __ ____ makes the Internet easy to navigate. The main Internet service functions are ______, ______,______ and ______.3.2 Website DesignTask 1: Finish a website according to the knowledge you have learned, and then describe the process in English.Task 2: Write down the six step tutorial helps you develop a premier website in you own words.Task 3: Discuss the website development in group. Explain how many have you exercised in your course of dynamic website design.Task 4: How many methods have been referred to create balanced page layout in the text? Which method have you often used in your experience? Write down why you have used the method so often.Many people wish they had software to create a flashy website. But creating a great website doesn’t happen at the tips of the fingers; it happens in the depths of the brain. Outst anding websites result from extensive planning. Prior preparation saves time and avoids frustration both during page creation and when updates and additions are required. Based on recommendations by professional web designers, the 6-step design tutorial helps you develop a premier website.Website Design Steps1. Establish an identity and use it consistently on all pagesIt doesn’t mean every page looks the same, but the colors and graphics we use should be consistent throughout the website. Even before knowing the number or type of pages, or a navigation scheme, create a homepage template and three or four sub-page templates using the chosen colors and graphics in combinations that are eye-catching and carrying forth your identity.2. Determine who uses your site and their information needsSuccessful websites know who their customers are and why they visit, and they provide aresponsive and attractive display to those viewers. Customers don’t visit our site because we spend time creating it; customers deserve maximum benefit from the time they allocate to us.3. Create user-friendly navigationOn a well-planned website it’s quick and easy to get to information pages—that’s navigation. Plan navigation before pages are created. Establish a navigation plan now to ensure that viewers quickly get what they need, and that webmasters can quickly insert new pages of content.4. Page layoutTo begin layout, analyze the information to be displayed and decide how it will be most readable. Pick the template that best accommodates that display. As your templates were created, page layout may have been anticipated. There are three methods to create balanced page layout: blockquote margins, tables, and frames. Each method has pros and cons; it can be advantageous to use all three to build a website.5. Focus on textThe best websites pack essential information into well-organized and well-written text. WebPages should not, however, be too heavily texted. Surveys show that web users will not read long paragraphs of information. They prefer concise, bite-sized sections, clearly delineated so they can scan for the information they need. You should write essential content as clearly and concisely as possible with brief topic headers.6. Use graphic images to enhance, not overpowerGraphics are a special challenge for web designers, requiring balance between overuse and skimpiness. A site filled with graphic images can have charm and impact. The secret for effective graphics is to stick to the theme and identity of the website.Website Development1. Interactive Dynamic WebsitesWebsites have grown from static online to become interactive dynamic sites. This is usually achieved by the use of a database, which is linked to the WebPages to serve content on the fly. The creation of sites involves programming skills and is known as website development.2. Real Time UpdatesThis means that sites can be updated in real time. For example a Discount Travel site could show availability and prices of all their available flight packages. When a particular flight is fullybooked the site would show this. Another example of website development would be an auction site that has constantly changing prices in response to bids from auction visitors to the site.3. Database ApplicationsThere are thousands of more applications for database website development and in the near future database driven sites are going to become the norm. This is partly due to the fact that web development sites are growing larger all the time and it is unpractical to update a large site in any other way. The most common databases for website development are Access, for simple databases; SQL, for more complex databases and Oracle, for the largest, most complex jobs.4. Active Server PagesASP (Active Server Pages ) —a programming language based on server side—as the best solution to create and implement dynamic websites is introduced in 1996 by Microsoft. Active Server Pages is an open, compile-free application environment in which you can combine HTML, Scripts and reusable ActiveX server components to create dynamic and powerful Web-based business solutions.Key Termstemplate n.(=templet)样板，模板navigation n.导航webmaster n.站点管理员，网络设计师compile v.编译static adj.静态的dynamic website 动态网站real time update 实时更新database application 数据库应用程序ASP(Active Server Pages) 动态服务器页面Vocabularywebsite n.网站prior adj.优先的，在先的frustration n.挫折，挫败，受挫premier adj.首要的，第一的n.总理homepage n.主页allocate v.分配，分派layout n.布置，安排，规划，设计accommodate v.容纳，适应，供给，供应blockquote margin 块边缘frame n.框架pros and cons 优缺点concise adj.简明的，简洁的delineate v.描绘，叙述，描写auction n.拍卖v.拍卖norm n.标准，规范TrainingI. Translate the following sentences.1. Prior preparation saves time and avoids frustration both during page creation and when updates and additions are required.2. Even before knowing the number or type of pages, or a navigation scheme, create a homepage template and three or four sub-page templates using the chosen colors and graphics in combinations that are eye-catching and carrying forth your identity.3. Establish a navigation plan now to ensure that viewers quickly get what they need, and that webmasters can quickly insert new pages of content.4. There are three methods to create balanced page layout: blockquote margins, tables, and frames. Each method has pros and cons; it can be advantageous to use all three to build a website.5. They prefer concise, bite-sized sections, clearly delineated so they can scan for the information they need.6. The secret for effective graphics is to stick to the theme and identity of the website.7. Websites have grown from static online to become interactive dynamic sites.8. Another example of website development would be an auction site that has constantlychanging prices in response to bids from auction visitors to the site.9. There are thousands of more applications for database website development and in the near future database driven sites are going to become the norm.10. Active Server Pages is an open, compile-free application environment in which you can combine HTML, Scripts and reusable ActiveX server components to create dynamic and powerful Web-based business solutions.II. Fill in the blanks with proper words.1. This passage introduces 6-step website design tutorial, which includes ,, , , and.2. Nowadays the most popular language to create dynamic website is . Use some words to describe it.3.3 Five Most Common Web Design MistakesTask 1: Design a website everybody and then discuss the mistakes group by group. Then compare with the text write down your own idea about the web design mistakes.Task 2: Combine with the knowledge you have learned explain the function of counters and banners.As you’re designing your new web site,you’ll be tempted with web design ideas that could turn into fatal mistakes. Below are five of the most common mistakes to avoid at all costs...1. Too Many GraphicsHaving too many graphics (particularly large graphics),can cause your site to load entirely too slow, Visitors will get impatient and oftentimes click out of your site—never to return.SOLUTION: When possible save your graphics as GIF files rather than JPEG, Also, reduce your graphic in actual size as much as you can without distorting the graphic or picture.2. CountersA visitor counter or hits counter should not be seen on your site unless you have tremendous traffic. The reason for this is that visitors really don’t want to know which visitor they are, especially if they’re Visitor number four. There’s no benefit to your visitor,nor is there anybenefit to you. The only way showing a counter is advantageous is if you’ve had millions of visitors and wish to display the popularity of your site or would like to attract advertisers with the large numbers.Otherwise, you can use this space for a headline that leads your visitor to another part of your site.SOLUTION: Most web hosts offer web statistics that reveal daily visitors,hits, etc. This feature will let you know how many people are visiting your site without me whole world seeing the information.If you’re just starting out, make sure your web host offers this free service.3. BannersLimit your banners to the bare necessities.Why? Because banners are graphics that can slow loading time and are a turn-off for many surfers on the Internet.For most,“banner” is just another word for“ad” and they avoid clicking on them.SOLUTION: If you do have a banner or two, place the banner at the very top or bottom of your page. Or you could place a small banner in your sidebar. Most people will look at the first picture they see and then start reading below the picture, so any writing or links that are above the banner may remain unnoticed.Also, the banners on your site should be related to your product or service.Remember, everything on your site should work together to benefit your target customer.4. Scattered Web SiteWhen designing your site, make sure it has a pattern that leads your visitor. Get several people (friends or relatives) to visit your site and watch them as they navigate. Notice the places where they stop and links that they click on. Organizing your site to lead visitors is very important whether you’re leading them to buy something or just to click and go to another place in your site.SOLUTION: Make sure t hat graphics don’t get in the way of your lead. If the visitor stops in the middle of the home page to click on a graphic or banner before getting to your sales page, they may never return.5. GeneralizationThe most effective way of selling on the Internet is to personalize your web site to reach your target audience. Many web sites are general and try to reach everybody. The reality is that you can’t be everything to everybody. The business owners who are successful on the web normally have very specific products or services that target a niche market.SOLUTION: Make your site as personal as possible. As you’re writing pretend that you areface 10 face with the customer. Present your web site in such a way that the visitor feels like he just walked into a store in his hometown. Also. stay focused on your target customer (one who would be inte rested in “your” product.)These five mistakes should be avoided at all costs if you want to build an effective and successful web business.Vocabularyfatal adj.致命的，毁灭性的avoid v.避免，消除at all cost 不惜任何代价，无论如何oftentimes adv.时常地distort v.歪曲，扭曲，弄歪counter n.计数器，计算器tremendous adj.巨大的，极大的traffic n.流量，访问量headline n.大字标题host n.主机reveal v.展示，展现，揭示，暴露banner n.旗帜，横幅，标语turn off <口语>令人厌烦的事物sidebar n.边注，其他选项，工具条personalize v.使成私人的，人格化niche market 有利可图的市场TrainingI. Translate the following sentences.1. Having too many graphics (particularly large graphics),can cause your site to load entirely too slow, Visitors will get impatient and oftentimes click out of your site—never to return.2. The only way showing a counter is advantageous is if you've had millions of visitors and wish to display the popularity of your site or would like to attract advertisers with the large numbers.Otherwise, you can use this space for a headline that leads your visitor to another part ofyour site.3. Limit your banners to the bare necessities.Why? Because banners are graphics that can slow loading time and are a turn-off for many surfers on the Internet.4. If the visitor stops in the middle of the home page to click on a graphic or banner before getting to your sales page, they may never return.5. The most effective way of selling on the Internet is to personalize your web site to reach your target audience.II. Write T (true) or F (false) for each statement.1. When possible save your graphics as JPEG files rather than GIF.2. A hits counter should not be seen on your site unless you have tremendous traffic.3. Most people on the Internet are interested in banner.4. Your web site can be very general and be everything to everybody.。

ImageProcessing2-ImageProcessingFundamentals√

16 of 70
Reflected Light
The colours that we perceive are determined by the nature of the light reflected from an object For example, if white light is shone onto a green object most wavelengths are absorbed, while green light is reflected from the object
5 of 70
Blind-Spot Experiment
Draw an image similar to that below on a piece of paper (the dot and cross are about 6 inches apart)
Close your right eye and focus on the cross with your left eye Hold the image about 20 inches away from your face and move it slowly towards you The dot should disappear!
4 of 70
Structure Of The Human Eye
The lens focuses light from objects onto the retina The retina is covered with light receptors called cones (6-7 million) and rods (75-150 million) Cones are concentrated around the fovea and are very sensitive to colour. Rods are more spread out and are sensitive to low levels of illumination

基于深度学习的图像分割技术分析

算注语言信IB与电厢China Computer&Communication2020年第23期基于深度学习的图像分割技术分析张影（苏州科技大学电子与信息工程学院，江苏苏州215009）摘要：近年来，深度学习已广泛应用在计算机视觉中，涵盖了图像分割、特征提取以及目标识别等方面，其中图像分割问题一直是一个经典难题。

本文主要对基于深度学习的图像分割技术的方法和研究现状进行了归纳总结，并就深度学习的图像处理技术进行详细讨论，主要从4个角度讨论处理图像分割的方法，最后对图像分割领域的技术发展做了总结。

关键词：深度学习；图像分割；深度网络中图分类号：TP391.4文献标识码：A文章编号：4003-9767（2020）23-068-02Research Review on Image Segmentation Based on Deep LearningZHANG Ying(College of Electronics and Information Engineering,Suzhou University of Science and Technology,Suzhou Jiangsu215009,China) Abstract：In recent years,deep learning has been widely used in computer vision,covering image segmentation,feature extraction and target recognition,among which image segmentation has always been a classic problem.In this paper,the methods and research status of image segmentation technology based on deep learning are summarized,and the image processing technology of deep learning is discussed in detail.The methods of image segmentation are mainly discussed from four aspects.Finally,the development of image segmentation technology is summarized.Keywords:deep learning;image segmentation;deep network0引言在计算机视觉中，图像处理、模式识别和图像识别都是近几年的研究热点，基于深度学习类型的分割有分类定位、目标检测、语义分割等。

基于WEB的多媒体素材管理库的开发与应用

基于WEB的多媒体素材管理库的开发与应用LT4.5数据的修改和删除 (23)第五章设计过程中的问题探讨和研究 (25)第六章结束语 (26)参考文献 (27)致谢 (2)8基于WEB的多媒体素材管理库的开发与应用摘要多媒体素材库对计算机辅助教学有着重要意义。

本文从建设素材库的意义出发，论述了当前多媒体素材库的现状及发展趋势，进而研究多媒体素材库的整体框架和库系统的设计，并详细的阐述了索引、上传文件及修改删除文件等功能的实现方法，运用ASP较系统的设计实现了一个基于web的多媒体素材管理库。

关键词：多媒体素材管理库 ASPThe development and application of the management storehouse in material of multimedia based on WEBAbstractThe multimedia material storehouse is significant for computer-assisted instruction. In this text , from buildt material meaning of storehouse set out , expound the current situations and development trends of multimedia material storehouse, and then study the whole frame of the multimedia material storehouse and design of the storehouse system, and detailed exposition implementation method of search , upload file , modify and delete file ,etc, use ASP more systematic design to realize that manage the storehouse in a multimedia material based on web.Key word：multimedia material manage storehouse ASP第一章基于WEB的多媒体素材管理库的开发1.1多媒体素材管理库开发的目的和意义当前，互联网的迅猛发展，多媒体技术得到普及。

基于深度CNN和极限学习机相结合的实时文档分类

基于深度CNN和极限学习机相结合的实时文档分类闫河;王鹏;董莺艳;罗成;李焕【摘要】提出一种文档图像实时分类训练和测试的方法.在实际应用中,数据训练的精确性和高效性在文档图像识别中起着关键的作用.现有的深度学习方法不能满足此要求,因为需要大量的时间用于训练和微调深层次的网络架构.针对此问题,提出一种基于计算机视觉的新方法:第一阶段训练深度网络,作为特征提取器;第二阶段用极限学习机(ELM)用于分类.该方法的性能优于目前最先进的基于深度学习的相关方法,在Tobacco-3482数据集上的最终准确率为83.45％.与之前基于卷积神经网络(CNN)的方法相比,相对误差降低了26％.ELM的训练时间仅为1.156秒,对2 482张图像的整体预测时间是3.083秒.因此,该文档分类方法适合于大规模实时应用.【期刊名称】《计算机应用与软件》【年(卷),期】2019(036)003【总页数】6页(P174-179)【关键词】文档图像分类;CNN;迁移学习【作者】闫河;王鹏;董莺艳;罗成;李焕【作者单位】重庆理工大学计算机科学与工程学院重庆401320;重庆理工大学两江人工智能学院重庆401147;重庆理工大学计算机科学与工程学院重庆401320;重庆理工大学计算机科学与工程学院重庆401320;重庆理工大学计算机科学与工程学院重庆401320;重庆理工大学计算机科学与工程学院重庆401320【正文语种】中文【中图分类】TP391.410 引言如今，商业文件(见图1)通常由文档分析系统(DAS)进行处理，以减少工作人员的工作量。

DAS的一项重要任务是对文档进行分类，即确定文档所指的业务流程的类型。

典型的文档类是发票、地址变更或索赔等。

文档分类方法可分为基于图像[1-6]和基于内容的方法[7-8]。

DAS选取哪一种方法更合适，通常取决于用户处理的文档。

像通常的字母一样，自由格式的文档通常需要基于内容的分类，而在不同布局中包含相同文本的表单则可以通过基于图像的方法来区分。

Nature Research Reporting Summary说明书

October 2018Corresponding author(s):Sinem K. Saka, Yu Wang, Peng YinLast updated by author(s):June 05, 2019Reporting SummaryNature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency in reporting. For further information on Nature Research policies, see Authors & Referees and the Editorial Policy Checklist .StatisticsFor all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.The exact sample size (n ) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedlyThe statistical test(s) used AND whether they are one- or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals)For null hypothesis testing, the test statistic (e.g. F , t , r ) with confidence intervals, effect sizes, degrees of freedom and P value notedGive P values as exact values whenever suitable.For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settingsFor hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomesEstimates of effect sizes (e.g. Cohen's d , Pearson's r ), indicating how they were calculatedOur web collection on statistics for biologists contains articles on many of the points above.Software and codePolicy information about availability of computer codeData collection Commercial softwares licensed by microscopy companies were utilized: Zeiss Zen 2012 (for LSM 710), Leica LAS AF (for Leica SP5), ZeissZen 2.3 Pro Blue edition (for LZeiss Axio Observer Z1), Olympus VS-ASW (for Olympus VS120), PerkinElmer Phenochart (version 1.0.2) .Data analysis Open-source Python (3.6.5), TensorFlow (1.12.0), and Deep Learning packages have been utilized for machine learning-based nucleiidentification (the algorithm and code is available at https:///HMS-IDAC/UNet). We used Matlab (2017b) for watershed-based nuclear segmentation using the identified nuclear contours. Python 3.6 was used for the FWHM calculations, as well as plotting ofhistograms. We used MATLAB and the Image Processing Toolbox R2016a (The MathWorks, Inc., Natick, Massachusetts, United States)for quantifications in mouse retina sections and for Supplementary Fig. 4. We utilized Cell Profiler 3.1.5 for the quantifications of signalamplification in FFPE samples in Figure 2 and 3. FIJI (version 2.0.0-rc-69/1.52n) was utilized for ROI selections and format conversions.HMS OMERO (version 5.4.6.21) was used for viewing images and assembling figure panels.For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.DataPolicy information about availability of dataAll manuscripts must include a data availability statement . This statement should provide the following information, where applicable:- Accession codes, unique identifiers, or web links for publicly available datasets- A list of figures that have associated raw data- A description of any restrictions on data availabilityData and Software Availability: The data and essential custom scripts for image processing will be made available from the corresponding authors P.Y.(**************.edu),S.K.S.(***********************.edu),andY.W.(********************.edu)uponrequest.Thedeeplearningalgorithmandtestdataset for automated identification of nuclear contours in tonsil tissues is available on https:///HMS-IDAC/UNet . The MATLAB code for nuclear segmentation isOctober 2018available on: https:///HMS-IDAC/SABERProbMapSegmentation .Field-specific reportingPlease select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.Life sciencesBehavioural & social sciences Ecological, evolutionary & environmental sciencesFor a reference copy of the document with all sections, see /documents/nr-reporting-summary-flat.pdfLife sciences study design All studies must disclose on these points even when the disclosure is negative.Sample size Each FFPE experiment batch were performed on consecutive sections from the same source, each containing over 600,000 cells. Due to largenumber of single cells with tens of distinct germinal center morphologies being present in each section, ROIs from different parts of a wholesection was used for quantification of signal improvement for each condition (consecutive sections were used for all the conditions of onequantification experiment). Number of ROIs are noted in the respective figure legends. For quantifications in retina samples, due toconserved staining morphology and low sample-to-sample variability n = 6 z-stacks were acquired from at least 2 retina sections. ForSupplementary Fig. 4, minimum 5 z-stacks were acquired for each condition to collect images of 18-45 cells. Number of cells are reported in the graphs.Data exclusions Parts of the FFPE tissue sections were excluded from analysis due to automated imaging related aberrations (out-of-focus areas) or tissuepreparation aberrations (folding of the thin sections at the edges, or uneven thickness at the edge areas). For FWHM calculations inSupplementary Fig. 2, ROIs that yield lineplots with more than one automatically detected peak were discarded to avoid deviations due tomultiple peaks. For Supplementary Fig. 4 cells in the samples were excluded when an external bright fluorescent particle (dust speck, dye aggregate etc.) coincided with the nuclei (as confirmed by manual inspection of the images). The exclusion criteria were pre-established.Replication Each FFPE experiment batch were performed on consecutive sections from the same source, each containing over 600,000 cells. Forevaluation and quantification of our method, multiple biological replicates were not accumulated in order to avoid the error that would beintroduced by the natural biological and preparation variation, and to avoid unnecessary use of human tissue material. In the case of themouse retina quantifications a minimum of two distinct retinal sections were imaged, and each experiment was performed at least twice. ForSupplementary Fig. 4 dataset, 16 different conditions were prepared and each were imaged multiple times (before linear, after linear, beforebranch, after branch). Although the data was not pooled together for the statistics reported in the figure, low cell-to-cell variability was observed and high consistency was seen across the samples for comparable conditions, suggesting low sample to sample variability.Randomization Randomization was not necessary for this study.Blinding Blinding was not possible as experimental conditions were mostly evident from the image data.Reporting for specific materials, systems and methodsWe require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material, system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.AntibodiesAntibodies used The full list is also available in Supplementary Information, Supplementary Table 4.Ki-67 Cell Signaling #9129, clone: D3B5 (formulated in PBS, Lot: 2), diluted 1:100-1:250 after conjugationCD8a Cell Signaling #85336 clone: D8A8Y (formulated in PBS, Lot: 4) diluted 1:150 after conjugationPD-1 Cell Signaling #43248, clone: EH33 (formulated in PBS, Lot: 2), diluted 1:150 after conjugationIgA Jackson ImmunoResearch #109-005-011 (Lot: 134868), diluted 1:150 after conjugationCD3e Cell Signaling #85061 clone: D7A6E(TM) XP(R) (formulated in PBS, Lot:2), diluted 1:150 after conjugationIgM Jackson ImmunoResearch #709-006-073 (Lot: 133627), diluted 1:150 after conjugationLamin B Santa Cruz sc-6216 clone:C-20, (Lot: E1115), diluted 1:100Alpha-Tubulin ThermoFisher #MA1-80017 (multiple lots), diluted 1:50 after conjugationCone arrestin Millipore #AB15282 (Lot: 2712407), diluted 1:100 after conjugationGFAP ThermoFisher #13-0300 (Lot: rh241999), diluted 1:50 after conjugationSV2 HybridomaBank, Antibody Registry ID: AB_2315387, in house production, diluted 1:25 after conjugationPKCα Novus #NB600-201, diluted 1:50 after conjugationCollagen IV Novus #NB120-6586, diluted 1:50 after conjugationRhodopsin EnCor Bio #MCA-A531, diluted 1:50 after conjugationCalbindin EnCor Bio #MCA-5A9, diluted 1:25 after conjugationVimentin Cell Signaling #5741S, diluted 1:50 after conjugationCalretinin EnCor Bio #MCA3G9, diluted 1:50 after conjugationVLP1 EnCor Bio #MCA-2D11, diluted 1:25 after conjugationBassoon Enzo ADI-VAM-#PS003, diluted 1:500Homer1b/c ThermoFisher #PA5-21487, diluted 1:250SupplementaryAnti-rabbit IgG (to detect Ki-67 and Homer1b/c indirectly) Jackson ImmunoResearch # 711-005-152 (Multiple lots), 1:90 afterconjugationAnti-mouse IgG (to detect Bassoon indirectly) Jackson ImmunoResearch #715-005-151) (Multiple lots), diluted 1:100 afterconjugationAnti-goat IgG (to detect Lamin B indirectly) Jackson ImmunoResearch # 705-005-147) (Lot: 125860), diluted 1:75 afterconjugationAlternative antibodies used to validate colocalization of VLP1 and Calretinin in Supplementary Fig. 8d-f:Calretinin (SantaCruz #SC-365956; EnCor Bio #CPCA-Calret; EnCor Bio #MCA-3G9 AP), VLP1 (EnCor Bio #RPCA-VLP1; EnCor Bio#CPCA-VLP1; EnCor Bio #MCA-2D11). All diluted 1:100.Fluorophore-conjugated secondary antibodies used for reference imaging:anti-rat-Alexa647 (ThermoFisher #A-21472, 1:200), anti-rabbit-Alexa488 (ThermoFisher #A-21206, 1:200), anti-rabbit-Atto488(Rockland #611-152-122S, Lot:33901, 1:500), anti-mouse-Alexa647 (ThermoFisher #A-31571, 1:400), anti-goat-Alexa647(ThermoFisher # A-21447, 1:200), anti-rabbit-Alexa647 (Jackson ImmunoResearch, 711-605-152, Lot: 125197, 1:300).Validation All antibodies used are from commercial sources as described. Only antibodies that have been validated by the vendor with in vitro and in situ experiments (for IHC and IF, with images available on the websites) and/or heavily used by the community withpublication in several references were used. The validation and references for each are publicly available on the respectivevendor websites that can reached via the catalog numbers listed above. In our experiments, IF patterns matched the distributionof cell types these antibodies were expected to label based on the literature both before and after conjugation with DNA strands. Eukaryotic cell linesPolicy information about cell linesCell line source(s)BS-C-1 cells and HeLa cellsAuthentication Cell lines were not authenticated (not relevant for the experiment or results)Mycoplasma contamination Cell lines were not tested for mycoplasma contamination (not relevant for the experiment or results)Commonly misidentified lines (See ICLAC register)No commonly misidentified cell lines were used.October 2018Animals and other organismsPolicy information about studies involving animals; ARRIVE guidelines recommended for reporting animal researchLaboratory animals Wild-type CD1 mice (male and female) age P13 or P17 were used for retina harvest.Wild animals The study did not involve wild animals.Field-collected samples The study did not involve samples collected from the field.Ethics oversight All animal procedures were in accordance with the National Institute for Laboratory Animal Research Guide for the Care and Useof Laboratory Animals and approved by the Harvard Medical School Committee on Animal Care.Note that full information on the approval of the study protocol must also be provided in the manuscript.Human research participantsPolicy information about studies involving human research participantsPopulation characteristics We have only used exempt tissue sections for technical demonstration, since we do not derive any biological conclusions, thepopulation characteristics is not relevant for this methodological study.Recruitment Not relevant for this study.Ethics oversight Human specimens were retrieved from the archives of the Pathology Department of Beth Israel Deaconess Medical Centerunder the discarded/excess tissue protocol as approved in Institutional Review Board (IRB) Protocol #2017P000585. Informedinform consent was waived on the basis of minimal risk to participants (which is indirect and not based on prospectiveparticipation by patients).Note that full information on the approval of the study protocol must also be provided in the manuscript.October 2018。

数字图像处理习题讲解

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第一章第5题
(c) 如果 D=0.3 mm,你能否使用2倍过采样？3倍过采样？. 可以， ∵ Δt < D/2 =0.15mm, Δt < D/3 =0.1mm
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滤波器频响零响应！
滤波器频响负响应！
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第三章习题8 (a)
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第一章习题2 一台光导摄像管摄像机的靶直径为25mm，感应点直径为35微米。若像素间距与点直径相同，它数字化一幅正方形图像时的最大行数和列数是多少？若要数字化的图像为480 ×640像素，靶上的最大像素间距是多少？

特征更新的动态图卷积表面损伤点云分割方法

第41卷　第4期吉林大学学报(信息科学版)Vol.41　No.42023年7月Journal of Jilin University (Information Science Edition)July 2023文章编号:1671⁃5896(2023)04⁃0621⁃10特征更新的动态图卷积表面损伤点云分割方法收稿日期:2022⁃09⁃21基金项目:国家自然科学基金资助项目(61573185)作者简介:张闻锐(1998 ),男,江苏扬州人,南京航空航天大学硕士研究生,主要从事点云分割研究,(Tel)86⁃188****8397(E⁃mail)839357306@;王从庆(1960 ),男,南京人,南京航空航天大学教授,博士生导师,主要从事模式识别与智能系统研究,(Tel)86⁃130****6390(E⁃mail)cqwang@㊂张闻锐,王从庆(南京航空航天大学自动化学院,南京210016)摘要:针对金属部件表面损伤点云数据对分割网络局部特征分析能力要求高,局部特征分析能力较弱的传统算法对某些数据集无法达到理想的分割效果问题,选择采用相对损伤体积等特征进行损伤分类,将金属表面损伤分为6类,提出一种包含空间尺度区域信息的三维图注意力特征提取方法㊂将得到的空间尺度区域特征用于特征更新网络模块的设计,基于特征更新模块构建出了一种特征更新的动态图卷积网络(Feature Adaptive Shifting⁃Dynamic Graph Convolutional Neural Networks)用于点云语义分割㊂实验结果表明,该方法有助于更有效地进行点云分割,并提取点云局部特征㊂在金属表面损伤分割上,该方法的精度优于PointNet ++㊁DGCNN(Dynamic Graph Convolutional Neural Networks)等方法,提高了分割结果的精度与有效性㊂关键词:点云分割;动态图卷积;特征更新;损伤分类中图分类号:TP391.41文献标志码:A Cloud Segmentation Method of Surface Damage Point Based on Feature Adaptive Shifting⁃DGCNNZHANG Wenrui,WANG Congqing(School of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)Abstract :The cloud data of metal part surface damage point requires high local feature analysis ability of the segmentation network,and the traditional algorithm with weak local feature analysis ability can not achieve the ideal segmentation effect for the data set.The relative damage volume and other features are selected to classify the metal surface damage,and the damage is divided into six categories.This paper proposes a method to extract the attention feature of 3D map containing spatial scale area information.The obtained spatial scale area feature is used in the design of feature update network module.Based on the feature update module,a feature updated dynamic graph convolution network is constructed for point cloud semantic segmentation.The experimental results show that the proposed method is helpful for more effective point cloud segmentation to extract the local features of point cloud.In metal surface damage segmentation,the accuracy of this method is better than pointnet++,DGCNN(Dynamic Graph Convolutional Neural Networks)and other methods,which improves the accuracy and effectiveness of segmentation results.Key words :point cloud segmentation;dynamic graph convolution;feature adaptive shifting;damage classification 0　引　言基于深度学习的图像分割技术在人脸㊁车牌识别和卫星图像分析领域已经趋近成熟,为获取物体更226吉林大学学报(信息科学版)第41卷完整的三维信息,就需要利用三维点云数据进一步完善语义分割㊂三维点云数据具有稀疏性和无序性,其独特的几何特征分布和三维属性使点云语义分割在许多领域的应用都遇到困难㊂如在机器人与计算机视觉领域使用三维点云进行目标检测与跟踪以及重建;在建筑学上使用点云提取与识别建筑物和土地三维几何信息;在自动驾驶方面提供路面交通对象㊁道路㊁地图的采集㊁检测和分割功能㊂2017年,Lawin等[1]将点云投影到多个视图上分割再返回点云,在原始点云上对投影分割结果进行分析,实现对点云的分割㊂最早的体素深度学习网络产生于2015年,由Maturana等[2]创建的VOXNET (Voxel Partition Network)网络结构,建立在三维点云的体素表示(Volumetric Representation)上,从三维体素形状中学习点的分布㊂结合Le等[3]提出的点云网格化表示,出现了类似PointGrid的新型深度网络,集成了点与网格的混合高效化网络,但体素化的点云面对大量点数的点云文件时表现不佳㊂在不规则的点云向规则的投影和体素等过渡态转换过程中,会出现很多空间信息损失㊂为将点云自身的数据特征发挥完善,直接输入点云的基础网络模型被逐渐提出㊂2017年,Qi等[4]利用点云文件的特性,开发了直接针对原始点云进行特征学习的PointNet网络㊂随后Qi等[5]又提出了PointNet++,针对PointNet在表示点与点直接的关联性上做出改进㊂Hu等[6]提出SENET(Squeeze⁃and⁃Excitation Networks)通过校准通道响应,为三维点云深度学习引入通道注意力网络㊂2018年,Li等[7]提出了PointCNN,设计了一种X⁃Conv模块,在不显著增加参数数量的情况下耦合较远距离信息㊂图卷积网络[8](Graph Convolutional Network)是依靠图之间的节点进行信息传递,获得图之间的信息关联的深度神经网络㊂图可以视为顶点和边的集合,使每个点都成为顶点,消耗的运算量是无法估量的,需要采用K临近点计算方式[9]产生的边缘卷积层(EdgeConv)㊂利用中心点与其邻域点作为边特征,提取边特征㊂图卷积网络作为一种点云深度学习的新框架弥补了Pointnet等网络的部分缺陷[10]㊂针对非规律的表面损伤这种特征缺失类点云分割,人们已经利用各种二维图像采集数据与卷积神经网络对风扇叶片㊁建筑和交通工具等进行损伤检测[11],损伤主要类别是裂痕㊁表面漆脱落等㊂但二维图像分割涉及的损伤种类不够充分,可能受物体表面污染㊁光线等因素影响,将凹陷㊁凸起等损伤忽视,或因光照不均匀判断为脱漆㊂笔者提出一种基于特征更新的动态图卷积网络,主要针对三维点云分割,设计了一种新型的特征更新模块㊂利用三维点云独特的空间结构特征,对传统K邻域内权重相近的邻域点采用空间尺度进行区分,并应用于对金属部件表面损伤分割的有用与无用信息混杂的问题研究㊂对邻域点进行空间尺度划分,将注意力权重分组,组内进行特征更新㊂在有效鉴别外邻域干扰特征造成的误差前提下,增大特征提取面以提高局部区域特征有用性㊂1　深度卷积网络计算方法1.1　包含空间尺度区域信息的三维图注意力特征提取方法由迭代最远点采集算法将整片点云分割为n个点集:{M1,M2,M3, ,M n},每个点集包含k个点:{P1, P2,P3, ,P k},根据点集内的空间尺度关系,将局部区域划分为不同的空间区域㊂在每个区域内,结合局部特征与空间尺度特征,进一步获得更有区分度的特征信息㊂根据注意力机制,为K邻域内的点分配不同的权重信息,特征信息包括空间区域内点的分布和区域特性㊂将这些特征信息加权计算,得到点集的卷积结果㊂使用空间尺度区域信息的三维图注意力特征提取方式,需要设定合适的K邻域参数K和空间划分层数R㊂如果K太小,则会导致弱分割,因不能完全利用局部特征而影响结果准确性;如果K太大,会增加计算时间与数据量㊂图1为缺损损伤在不同参数K下的分割结果图㊂由图1可知,在K=30或50时,分割结果效果较好,K=30时计算量较小㊂笔者选择K=30作为实验参数㊂在分析确定空间划分层数R之前,简要分析空间层数划分所应对的问题㊂三维点云所具有的稀疏性㊁无序性以及损伤点云自身噪声和边角点多的特性,导致了点云处理中可能出现的共同缺点,即将离群值点云选为邻域内采样点㊂由于损伤表面多为一个面,被分割出的损伤点云应在该面上分布,而噪声点则被分布在整个面的两侧,甚至有部分位于损伤内部㊂由于点云噪声这种立体分布的特征,导致了离群值被选入邻域内作为采样点存在㊂根据采用DGCNN(Dynamic Graph Convolutional Neural Networks)分割网络抽样实验结果,位于切面附近以及损伤内部的离群值点对点云分割结果造成的影响最大,被错误分割为特征点的几率最大,在后续预处理过程中需要对这种噪声点进行优先处理㊂图1　缺损损伤在不同参数K 下的分割结果图Fig.1　Segmentation results of defect damage under different parameters K 基于上述实验结果,在参数K =30情况下,选择空间划分层数R ㊂缺损损伤在不同参数R 下的分割结果如图2所示㊂图2b 的结果与测试集标签分割结果更为相似,更能体现损伤的特征,同时屏蔽了大部分噪声㊂因此,选择R =4作为实验参数㊂图2　缺损损伤在不同参数R 下的分割结果图Fig.2　Segmentation results of defect damage under different parameters R 在一个K 邻域内,邻域点与中心点的空间关系和特征差异最能表现邻域点的权重㊂空间特征系数表示邻域点对中心点所在点集的重要性㊂同时,为更好区分图内邻域点的权重,需要将整个邻域细分㊂以空间尺度进行细分是较为合适的分类方式㊂中心点的K 邻域可视为一个局部空间,将其划分为r 个不同的尺度区域㊂再运算空间注意力机制,为这r 个不同区域的权重系数赋值㊂按照空间尺度多层次划分,不仅没有损失核心的邻域点特征,还能有效抑制无意义的㊁有干扰性的特征㊂从而提高了深度学习网络对点云的局部空间特征的学习能力,降低相邻邻域之间的互相影响㊂空间注意力机制如图3所示,计算步骤如下㊂第1步,计算特征系数e mk ㊂该值表示每个中心点m 的第k 个邻域点对其中心点的权重㊂分别用Δp mk 和Δf mk 表示三维空间关系和局部特征差异,M 表示MLP(Multi⁃Layer Perceptrons)操作,C 表示concat 函数,其中Δp mk =p mk -p m ,Δf mk =M (f mk )-M (f m )㊂将两者合并后输入多层感知机进行计算,得到计算特征系数326第4期张闻锐,等:特征更新的动态图卷积表面损伤点云分割方法图3　空间尺度区域信息注意力特征提取方法示意图Fig.3　Schematic diagram of attention feature extraction method for spatial scale regional information e mk =M [C (Δp mk ‖Δf mk )]㊂(1) 第2步,计算图权重系数a mk ㊂该值表示每个中心点m 的第k 个邻域点对其中心点的权重包含比㊂其中k ∈{1,2,3, ,K },K 表示每个邻域所包含点数㊂需要对特征系数e mk 进行归一化,使用归一化指数函数S (Softmax)得到权重多分类的结果,即计算图权重系数a mk =S (e mk )=exp(e mk )/∑K g =1exp(e mg )㊂(2) 第3步,用空间尺度区域特征s mr 表示中心点m 的第r 个空间尺度区域的特征㊂其中k r ∈{1,2,3, ,K r },K r 表示第r 个空间尺度区域所包含的邻域点数,并在其中加入特征偏置项b r ,避免权重化计算的特征在动态图中累计单面误差指向,空间尺度区域特征s mr =∑K r k r =1[a mk r M (f mk r )]+b r ㊂(3) 在r 个空间尺度区域上进行计算,就可得到点m 在整个局部区域的全部空间尺度区域特征s m ={s m 1,s m 2,s m 3, ,s mr },其中r ∈{1,2,3, ,R }㊂1.2　基于特征更新的动态图卷积网络动态图卷积网络是一种能直接处理原始三维点云数据输入的深度学习网络㊂其特点是将PointNet 网络中的复合特征转换模块(Feature Transform),改进为由K 邻近点计算(K ⁃Near Neighbor)和多层感知机构成的边缘卷积层[12]㊂边缘卷积层功能强大,其提取的特征不仅包含全局特征,还拥有由中心点与邻域点的空间位置关系构成的局部特征㊂在动态图卷积网络中,每个邻域都视为一个点集㊂增强对其中心点的特征学习能力,就会增强网络整体的效果[13]㊂对一个邻域点集,对中心点贡献最小的有效局部特征的边缘点,可以视为异常噪声点或低权重点,可能会给整体分割带来边缘溢出㊂点云相比二维图像是一种信息稀疏并且噪声含量更大的载体㊂处理一个局域内的噪声点,将其直接剔除或简单采纳会降低特征提取效果,笔者对其进行低权重划分,并进行区域内特征更新,增强抗噪性能,也避免点云信息丢失㊂在空间尺度区域中,在区域T 内有s 个点x 被归为低权重系数组,该点集的空间信息集为P ∈R N s ×3㊂点集的局部特征集为F ∈R N s ×D f [14],其中D f 表示特征的维度空间,N s 表示s 个域内点的集合㊂设p i 以及f i 为点x i 的空间信息和特征信息㊂在点集内,对点x i 进行小范围内的N 邻域搜索,搜索其邻域点㊂则点x i 的邻域点{x i ,1,x i ,2, ,x i ,N }∈N (x i ),其特征集合为{f i ,1,f i ,2, ,f i ,N }∈F ㊂在利用空间尺度进行区域划分后,对空间尺度区域特征s mt 较低的区域进行区域内特征更新,通过聚合函数对权重最低的邻域点在图中的局部特征进行改写㊂已知中心点m ,点x i 的特征f mx i 和空间尺度区域特征s mt ,目的是求出f ′mx i ,即中心点m 的低权重邻域点x i 在进行邻域特征更新后得到的新特征㊂对区域T 内的点x i ,∀x i ,j ∈H (x i ),x i 与其邻域H 内的邻域点的特征相似性域为R (x i ,x i ,j )=S [C (f i ,j )T C (f i ,j )/D o ],(4)其中C 表示由输入至输出维度的一维卷积,D o 表示输出维度值,T 表示转置㊂从而获得更新后的x i 的426吉林大学学报(信息科学版)第41卷特征㊂对R (x i ,x i ,j )进行聚合,并将特征f mx i 维度变换为输出维度f ′mx i =∑[R (x i ,x i ,j )S (s mt f mx i )]㊂(5) 图4为特征更新网络模块示意图,展示了上述特征更新的计算过程㊂图5为特征更新的动态图卷积网络示意图㊂图4　特征更新网络模块示意图Fig.4　Schematic diagram of feature update network module 图5　特征更新的动态图卷积网络示意图Fig.5　Flow chart of dynamic graph convolution network with feature update 动态图卷积网络(DGCNN)利用自创的边缘卷积层模块,逐层进行边卷积[15]㊂其前一层的输出都会动态地产生新的特征空间和局部区域,新一层从前一层学习特征(见图5)㊂在每层的边卷积模块中,笔者在边卷积和池化后加入了空间尺度区域注意力特征,捕捉特定空间区域T 内的邻域点,用于特征更新㊂特征更新会降低局域异常值点对局部特征的污染㊂网络相比传统图卷积神经网络能获得更多的特征信息,并且在面对拥有较多噪声值的点云数据时,具有更好的抗干扰性[16],在对性质不稳定㊁不平滑并含有需采集分割的突出中心的点云数据时,会有更好的抗干扰效果㊂相比于传统预处理方式,其稳定性更强,不会发生将突出部分误分割或漏分割的现象[17]㊂2　实验结果与分析点云分割的精度评估指标主要由两组数据构成[18],即平均交并比和总体准确率㊂平均交并比U (MIoU:Mean Intersection over Union)代表真实值和预测值合集的交并化率的平均值,其计算式为526第4期张闻锐,等:特征更新的动态图卷积表面损伤点云分割方法U =1T +1∑Ta =0p aa ∑Tb =0p ab +∑T b =0p ba -p aa ,(6)其中T 表示类别,a 表示真实值,b 表示预测值,p ab 表示将a 预测为b ㊂总体准确率A (OA:Overall Accuracy)表示所有正确预测点p c 占点云模型总体数量p all 的比,其计算式为A =P c /P all ,(7)其中U 与A 数值越大,表明点云分割网络越精准,且有U ≤A ㊂2.1　实验准备与数据预处理实验使用Kinect V2,采用Depth Basics⁃WPF 模块拍摄金属部件损伤表面获得深度图,将获得的深度图进行SDK(Software Development Kit)转化,得到pcd 格式的点云数据㊂Kinect V2采集的深度图像分辨率固定为512×424像素,为获得更清晰的数据图像,需尽可能近地采集数据㊂选择0.6~1.2m 作为采集距离范围,从0.6m 开始每次增加0.2m,获得多组采量数据㊂点云中分布着噪声,如果不对点云数据进行过滤会对后续处理产生不利影响㊂根据统计原理对点云中每个点的邻域进行分析,再建立一个特别设立的标准差㊂然后将实际点云的分布与假设的高斯分布进行对比,实际点云中误差超出了标准差的点即被认为是噪声点[19]㊂由于点云数据量庞大,为提高效率,选择采用如下改进方法㊂计算点云中每个点与其首个邻域点的空间距离L 1和与其第k 个邻域点的空间距离L k ㊂比较每个点之间L 1与L k 的差,将其中差值最大的1/K 视为可能噪声点[20]㊂计算可能噪声点到其K 个邻域点的平均值,平均值高出标准差的被视为噪声点,将离群噪声点剔除后完成对点云的滤波㊂2.2　金属表面损伤点云关键信息提取分割方法对点云损伤分割,在制作点云数据训练集时,如果只是单一地将所有损伤进行统一标记,不仅不方便进行结果分析和应用,而且也会降低特征分割的效果㊂为方便分析和控制分割效果,需要使用ArcGIS 将点云模型转化为不规则三角网TIN(Triangulated Irregular Network)㊂为精确地分类损伤,利用图6　不规则三角网模型示意图Fig.6　Schematic diagram of triangulated irregular networkTIN 的表面轮廓性质,获得训练数据损伤点云的损伤内(外)体积,损伤表面轮廓面积等㊂如图6所示㊂选择损伤体积指标分为相对损伤体积V (RDV:Relative Damege Volume)和邻域内相对损伤体积比N (NRDVR:Neighborhood Relative Damege Volume Ratio)㊂计算相对平均深度平面与点云深度网格化平面之间的部分,得出相对损伤体积㊂利用TIN 邻域网格可获取某损伤在邻域内的相对深度占比,有效解决制作测试集时,将因弧度或是形状造成的相对深度判断为损伤的问题㊂两种指标如下:V =∑P d k =1h k /P d -∑P k =1h k /()P S d ,(8)N =P n ∑P d k =1h k S d /P d ∑P n k =1h k S ()n -()1×100%,(9)其中P 表示所有点云数,P d 表示所有被标记为损伤的点云数,P n 表示所有被认定为损伤邻域内的点云数;h k 表示点k 的深度值;S d 表示损伤平面面积,S n 表示损伤邻域平面面积㊂在获取TIN 标准包络网视图后,可以更加清晰地描绘损伤情况,同时有助于量化损伤严重程度㊂笔者将损伤分为6种类型,并利用计算得出的TIN 指标进行损伤分类㊂同时,根据损伤部分体积与非损伤部分体积的关系,制定指标损伤体积(SDV:Standard Damege Volume)区分损伤类别㊂随机抽选5个测试组共50张图作为样本㊂统计非穿透损伤的RDV 绝对值,其中最大的30%标记为凹陷或凸起,其余626吉林大学学报(信息科学版)第41卷标记为表面损伤,并将样本分类的标准分界值设为SDV㊂在设立以上标准后,对凹陷㊁凸起㊁穿孔㊁表面损伤㊁破损和缺损6种金属表面损伤进行分类,金属表面损伤示意图如图7所示㊂首先,根据损伤是否产生洞穿,将损伤分为两大类㊂非贯通伤包括凹陷㊁凸起和表面损伤,贯通伤包括穿孔㊁破损和缺损㊂在非贯通伤中,凹陷和凸起分别采用相反数的SDV 作为标准,在这之间的被分类为表面损伤㊂贯通伤中,以损伤部分平面面积作为参照,较小的分类为穿孔,较大的分类为破损,而在边缘处因腐蚀㊁碰撞等原因缺角㊁内损的分类为缺损㊂分类参照如表1所示㊂图7　金属表面损伤示意图Fig.7　Schematic diagram of metal surface damage表1　损伤类别分类Tab.1　Damage classification 损伤类别凹陷凸起穿孔表面损伤破损缺损是否形成洞穿××√×√√RDV 绝对值是否达到SDV √√\×\\S d 是否达到标准\\×\√\2.3　实验结果分析为验证改进的图卷积深度神经网络在点云语义分割上的有效性,笔者采用TensorFlow 神经网络框架进行模型测试㊂为验证深度网络对损伤分割的识别准确率,采集了带有损伤特征的金属部件损伤表面点云,对点云进行预处理㊂对若干金属部件上的多个样本金属面的点云数据进行筛选,删除损伤占比低于5%或高于60%的数据后,划分并装包制作为点云数据集㊂采用CloudCompare 软件对样本金属上的损伤部分进行分类标记,共分为6种如上所述损伤㊂部件损伤的数据集制作参考点云深度学习领域广泛应用的公开数据集ModelNet40part㊂分割数据集包含了多种类型的金属部件损伤数据,这些损伤数据显示在510张总点云图像数据中㊂点云图像种类丰富,由各种包含损伤的金属表面构成,例如金属门,金属蒙皮,机械构件外表面等㊂用ArcGIS 内相关工具将总图进行随机点拆分,根据数据集ModelNet40part 的规格,每个独立的点云数据组含有1024个点,将所有总图拆分为510×128个单元点云㊂将样本分为400个训练集与110个测试集,采用交叉验证方法以保证测试的充分性[20],对多种方法进行评估测试,实验结果由单元点云按原点位置重新组合而成,并带有拆分后对单元点云进行的分割标记㊂分割结果比较如图8所示㊂726第4期张闻锐,等:特征更新的动态图卷积表面损伤点云分割方法图8　分割结果比较图Fig.8　Comparison of segmentation results在部件损伤分割的实验中,将不同网络与笔者网络(FAS⁃DGCNN:Feature Adaptive Shifting⁃Dynamic Graph Convolutional Neural Networks)进行对比㊂除了采用不同的分割网络外,其余实验均采用与改进的图卷积深度神经网络方法相同的实验设置㊂实验结果由单一损伤交并比(IoU:Intersection over Union),平均损伤交并比(MIoU),单一损伤准确率(Accuracy)和总体损伤准确率(OA)进行评价,结果如表2~表4所示㊂将6种不同损伤类别的Accuracy 与IoU 进行对比分析,可得出结论:相比于基准实验网络Pointet++,笔者在OA 和MioU 方面分别在贯通伤和非贯通伤上有10%和20%左右的提升,在整体分割指标上,OA 能达到90.8%㊂对拥有更多点数支撑,含有较多点云特征的非贯通伤,几种点云分割网络整体性能均能达到90%左右的效果㊂而不具有局部特征识别能力的PointNet 在贯通伤上的表现较差,不具备有效的分辨能力,导致分割效果相对于其他损伤较差㊂表2　损伤部件分割准确率性能对比 Tab.2　Performance comparison of segmentation accuracy of damaged parts %实验方法准确率凹陷⁃1凸起⁃2穿孔⁃3表面损伤⁃4破损⁃5缺损⁃6Ponitnet 82.785.073.880.971.670.1Pointnet++88.786.982.783.486.382.9DGCNN 90.488.891.788.788.687.1FAS⁃DGCNN 92.588.892.191.490.188.6826吉林大学学报(信息科学版)第41卷表3　损伤部件分割交并比性能对比 Tab.3　Performance comparison of segmentation intersection ratio of damaged parts %IoU 准确率凹陷⁃1凸起⁃2穿孔⁃3表面损伤⁃4破损⁃5缺损⁃6PonitNet80.582.770.876.667.366.9PointNet++86.384.580.481.184.280.9DGCNN 88.786.589.986.486.284.7FAS⁃DGCNN89.986.590.388.187.385.7表4　损伤分割的整体性能对比分析　　出,动态卷积图特征以及有效的邻域特征更新与多尺度注意力给分割网络带来了更优秀的局部邻域分割能力,更加适应表面损伤分割的任务要求㊂3　结　语笔者利用三维点云独特的空间结构特征,将传统K 邻域内权重相近的邻域点采用空间尺度进行区分,并将空间尺度划分运用于邻域内权重分配上,提出了一种能将邻域内噪声点降权筛除的特征更新模块㊂采用此模块的动态图卷积网络在分割上表现出色㊂利用特征更新的动态图卷积网络(FAS⁃DGCNN)能有效实现金属表面损伤的分割㊂与其他网络相比,笔者方法在点云语义分割方面表现出更高的可靠性,可见在包含空间尺度区域信息的注意力和局域点云特征更新下,笔者提出的基于特征更新的动态图卷积网络能发挥更优秀的作用,而且相比缺乏局部特征提取能力的分割网络,其对于点云稀疏㊁特征不明显的非贯通伤有更优的效果㊂参考文献:[1]LAWIN F J,DANELLJAN M,TOSTEBERG P,et al.Deep Projective 3D Semantic Segmentation [C]∥InternationalConference on Computer Analysis of Images and Patterns.Ystad,Sweden:Springer,2017:95⁃107.[2]MATURANA D,SCHERER S.VoxNet:A 3D Convolutional Neural Network for Real⁃Time Object Recognition [C]∥Proceedings of IEEE /RSJ International Conference on Intelligent Robots and Systems.Hamburg,Germany:IEEE,2015:922⁃928.[3]LE T,DUAN Y.PointGrid:A Deep Network for 3D Shape Understanding [C]∥2018IEEE /CVF Conference on ComputerVision and Pattern Recognition (CVPR).Salt Lake City,USA:IEEE,2018:9204⁃9214.[4]QI C R,SU H,MO K,et al.PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation [C]∥IEEEConference on Computer Vision and Pattern Recognition (CVPR).Hawaii,USA:IEEE,2017:652⁃660.[5]QI C R,SU H,MO K,et al,PointNet ++:Deep Hierarchical Feature Learning on Point Sets in a Metric Space [C]∥Advances in Neural Information Processing Systems.California,USA:SpringerLink,2017:5099⁃5108.[6]HU J,SHEN L,SUN G,Squeeze⁃and⁃Excitation Networks [C ]∥IEEE Conference on Computer Vision and PatternRecognition.Vancouver,Canada:IEEE,2018:7132⁃7141.[7]LI Y,BU R,SUN M,et al.PointCNN:Convolution on X⁃Transformed Points [C]∥Advances in Neural InformationProcessing Systems.Montreal,Canada:NeurIPS,2018:820⁃830.[8]ANH VIET PHAN,MINH LE NGUYEN,YEN LAM HOANG NGUYEN,et al.DGCNN:A Convolutional Neural Networkover Large⁃Scale Labeled Graphs [J].Neural Networks,2018,108(10):533⁃543.[9]任伟建,高梦宇,高铭泽,等.基于混合算法的点云配准方法研究[J].吉林大学学报(信息科学版),2019,37(4):408⁃416.926第4期张闻锐,等:特征更新的动态图卷积表面损伤点云分割方法036吉林大学学报(信息科学版)第41卷REN W J,GAO M Y,GAO M Z,et al.Research on Point Cloud Registration Method Based on Hybrid Algorithm[J]. Journal of Jilin University(Information Science Edition),2019,37(4):408⁃416.[10]ZHANG K,HAO M,WANG J,et al.Linked Dynamic Graph CNN:Learning on Point Cloud via Linking Hierarchical Features[EB/OL].[2022⁃03⁃15].https:∥/stamp/stamp.jsp?tp=&arnumber=9665104. [11]林少丹,冯晨,陈志德,等.一种高效的车体表面损伤检测分割算法[J].数据采集与处理,2021,36(2):260⁃269. LIN S D,FENG C,CHEN Z D,et al.An Efficient Segmentation Algorithm for Vehicle Body Surface Damage Detection[J]. Journal of Data Acquisition and Processing,2021,36(2):260⁃269.[12]ZHANG L P,ZHANG Y,CHEN Z Z,et al.Splitting and Merging Based Multi⁃Model Fitting for Point Cloud Segmentation [J].Journal of Geodesy and Geoinformation Science,2019,2(2):78⁃79.[13]XING Z Z,ZHAO S F,GUO W,et al.Processing Laser Point Cloud in Fully Mechanized Mining Face Based on DGCNN[J]. ISPRS International Journal of Geo⁃Information,2021,10(7):482⁃482.[14]杨军,党吉圣.基于上下文注意力CNN的三维点云语义分割[J].通信学报,2020,41(7):195⁃203. YANG J,DANG J S.Semantic Segmentation of3D Point Cloud Based on Contextual Attention CNN[J].Journal on Communications,2020,41(7):195⁃203.[15]陈玲,王浩云,肖海鸿,等.利用FL⁃DGCNN模型估测绿萝叶片外部表型参数[J].农业工程学报,2021,37(13): 172⁃179.CHEN L,WANG H Y,XIAO H H,et al.Estimation of External Phenotypic Parameters of Bunting Leaves Using FL⁃DGCNN Model[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(13):172⁃179.[16]柴玉晶,马杰,刘红.用于点云语义分割的深度图注意力卷积网络[J].激光与光电子学进展,2021,58(12):35⁃60. CHAI Y J,MA J,LIU H.Deep Graph Attention Convolution Network for Point Cloud Semantic Segmentation[J].Laser and Optoelectronics Progress,2021,58(12):35⁃60.[17]张学典,方慧.BTDGCNN:面向三维点云拓扑结构的BallTree动态图卷积神经网络[J].小型微型计算机系统,2021, 42(11):32⁃40.ZHANG X D,FANG H.BTDGCNN:BallTree Dynamic Graph Convolution Neural Network for3D Point Cloud Topology[J]. Journal of Chinese Computer Systems,2021,42(11):32⁃40.[18]张佳颖,赵晓丽,陈正.基于深度学习的点云语义分割综述[J].激光与光电子学,2020,57(4):28⁃46. ZHANG J Y,ZHAO X L,CHEN Z.A Survey of Point Cloud Semantic Segmentation Based on Deep Learning[J].Lasers and Photonics,2020,57(4):28⁃46.[19]SUN Y,ZHANG S H,WANG T Q,et al.An Improved Spatial Point Cloud Simplification Algorithm[J].Neural Computing and Applications,2021,34(15):12345⁃12359.[20]高福顺,张鼎林,梁学章.由点云数据生成三角网络曲面的区域增长算法[J].吉林大学学报(理学版),2008,46 (3):413⁃417.GAO F S,ZHANG D L,LIANG X Z.A Region Growing Algorithm for Triangular Network Surface Generation from Point Cloud Data[J].Journal of Jilin University(Science Edition),2008,46(3):413⁃417.(责任编辑:刘俏亮)。

FortiWeb Web Application Firewall（WAF）详细说明说明书

FAQFortiWeb: Web Application Firewall (WAF) Comprehensive, High-Performance Web Application SecurityCan’t an IPS or Firewall provide protection for hosted web-based applications?Next Generation and Application Aware IPS firewalls extend and enhance protection and add additional functionality but the majority ofthe ‘application aware’ functionality is focused on securing/restricting internal clients when accessing the internet but not securing internal applications from external threats. Web Application Firewalls are different as they protect internal web applications from sophisticated application layer external attacks. They provide both a positive and negative security model and protect against the major threats to applications today (SQL Injection, Cross Site Scripting, URL Access, CSRF, Injection attacks and more).Why is FortiWeb’s AI-based Machine Learning threat detection superior to other threat detection methods?Other vendors use application learning using an observational method to automate profile creation for web-based application protection. Application learning is a good detection method, but it has many drawbacks. These include:n high false-positive detectionsnnn labor-intensive to fine tunenn unobserved legitimate traffic creates anomaliesn aggressive tuning lets attacks slip through more easilynnn changes to the application require substantial re-learning to prevent false-positive detectionsFortiWeb’s behavioral detection uses two layers of AI-based machine learning and statistical probabilities to detect anomalies and threats separately. With machine learning FortiWeb is able to deliver near 100% application threat detection accuracy with virtually no resources required to manage it. AI-based machine learning for FortiWeb creates nearly a “set and forget” web application firewall that doesn’t sacrifice accuracy for ease of management.What size WAF do I need?There are many factors that determine WAF sizing ranging from application throughput, numbers of users, and number of sites to be protected. We strongly recommend discussing your requirements with a Fortinet Partner to find the best option to meet your needs.How does FortiWeb Cloud differ from an on-prem FortiWeb deployment?FortiWeb Cloud is a ‘skinny’ WAF solution offering negative security model rules while the FortiWeb platform is a full blown WAF offering both positive and negative security models. Most customers using a Cloud WAF are looking for a set-it-and-forget type solution that they can quickly configure and use without having to manage daily. By offering a subset of what FortiWeb on-prem offers but with a simple, straightforward configuration and management FortiWeb Cloud addresses these requirements.Do I need a WAF if I already have a Secure Web Gateway (SWG)?Yes. A SWG protects users within the organization from accessing infected external websites or undesirable content hosted outside of the organization. A WAF protects hosted web-based applications from attacks that are initiated by external attackers. A simplified view is the SWGs protect users and WAFs protect applications.1Copyright © 2019 Fortinet, Inc. All rights reserved. Fortinet , FortiGate , FortiCare and FortiGuard , and certain other marks are registered trademarks of Fortinet, Inc., and other Fortinet names herein may also be registered and/or common law trademarks of Fortinet. All other product or company names may be trademarks of their respective owners. Performance and other metrics contained herein were attained in internal lab tests under ideal conditions, and actual performance and other results may vary. Network variables, different network environments and other conditions may affect performance results. Nothing herein represents any binding commitment by Fortinet, and Fortinet disclaims all warranties, whether express or implied, except to the extent Fortinet enters a binding written contract, signed by Fortinet’s General Counsel, with a purchaser that expressly warrants that the identified product will perform according to certain expressly-identified performance metrics and, in such event, only the specific performance metrics expressly identified in such binding written contract shall be binding on Fortinet. For absolute clarity, any such warranty will be limited to performance in the same ideal conditions as in Fortinet’s internal lab tests. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be applicable. Fortinet disclaims in full any covenants, representations, and guarantees pursuant hereto, whether express or implied. Fortinet reserves the right to change, modify, transfer, or otherwise revise this publication without notice, and the most current version of the publication shall be February 19, 2019 9:51 AM Mac:Users:susiehwang:Desktop:Egnyte:Egnyte:Shared:Creative Services:Team:Susie-Hwang:Egnyte:Shared:CREATIVE SERVICES:Team:Susie-Hwang:2019:FAQ-FortiWeb:FAQ-FortiWeb-021919-950am FAQ | FortiWeb: Web Application Firewall (WAF)358956-0-0-EN FortiWeb WAF vs. WAF in an ADCA dedicated WAF appliance will not decrease performance, plus an appliance like FortiWeb has the processing power to perform behavior-based detection of application attacks. Most WAF modules on ADCs offer only basic WAF protection for applications.Can a FortiWeb permanently patch application vulnerabilities?Yes it can. FortiWeb can provide temporary application patching until development teams are able deploy permanent patches forvulnerabilities, or it can permanently patch them. It is usually recommended to permanently fix a known vulnerability, however there are many situations where that isn’t possible or practical, such as inherited applications or older applications that are about to be retired.。

多媒体应用设计师-专业英语_真题-无答案

多媒体应用设计师-专业英语(总分45,考试时间90分钟)DOM is a platform- and language- (66) API that allows programs and scripts to dynamically access and update the content, structure and style of WWW documents (currently, definitions for HTML and XML documents are part of the specification). The document can be further processed and the results of that processing can be incorporated back into the presented (67) . DOM is a (68) -based API to documents, which requires the whole document to be represented in (69) while processing it. A simpler alternative to DOM is the event-based SAX, which can be used to process very large (70) documents that do not fit into the memory available for processing.1.A. specificB. neutralC. containedD. related2.A. textB. ImageC. PageD. graphic3.A. tableB. treeC. controlD. event4.A. documentB. processorC. discD. memory5.A. XMLB. HTMLC. scriptD. WebMelissa and LoveLetter made use of the trust that exists between friends or colleagues. Imagine receiving an (71) from a friend who asks you to open it. This is what happens with Melissa and several other similar email (72) . Upon running, such worms usually proceed to send themselves out to email addresses from the victim's address book, previous emails, web pages (73) .As administrators seek to block dangerous email attachments through the recognition ofwell-known (74) , virus writers use other extensions to circumvent such protection. Executable (.exe) files are renamed to .bat and .cmd plus a whole list of other extensions and will still run and successfully infect target users.Frequently, hackers try to penetrate networks by sending an attachment that looks like a flash movie, which, while displaying some cute animation, simultaneously **mands in the background to steal your passwords and give the (75) access to your network.6.A. attachmentB. packetC. datagramD. message7.A. virtualB. virusC. wormsD. bacteria8.A. memoryB. cachesC. portsD. registers9.A. namesB. cookiesC. softwareD. extensions10.A. crackerB. userC. customerD. clientOriginally introduced by Netscape Communications, (66) are a general mechanism which HTTP Server side applications, such as CGI (67) , can use to both store and retrieve information on the HTTP (68) side of the connection. Basically, Cookies can be used to compensate for the (69) nature of HTTE The addition of a simple, persistent, client-side state significantly extends the capabilities of WWW-based (70) .11.A. BrowsersB. CookiesC. ConnectionsD. Scripts12.A. graphicsB. processesC. scriptsD. texts13.A. ClientB. EditorC. CreatorD. Server14.A. fixedB. flexibleC. stableD. stateless15.A. programsB. applicationsC. frameworksD. constrainsWebSQL is a SQL-like (71) language for extracting information from the web. Its capabilities for performing navigation of web (72) make it a useful tool for automating several web-related tasks that require the systematic processing of either all the links in a (73) , all the pages that can be reached from a given URL through (74) that match a pattern, or a combination of both. WebSQL also provides transparent access to index servers that can be queried via the Common (75) Interface.16.A. queryB. transactionC. communicationD. programming17.A. browsersB. serversC. hypertextsD. clients18.A. hypertextB. pageC. protocolD. operation19.A. pathsB. chipsC. toolsD. directories20.A. RouterB. DeviceC. ComputerD. GatewayTCP/IP (71) layer protocols provide services to the application (72) running on a computer. The application layer does not define the application itself, but rather it defines (73) that applications need — like the ability to transfer a file in the case of HTTE In short, the application layer provides an (74) between software running on a computer and the network itself. The TCP/IP application layer includes a relatively large number of protocols, with HTTP being only one of those. The TCP/IP (75) layer consists of two main protocol options — the Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP).21.A. applicationB. transportC. linkD. network22.A. hardwareB. softwareC. packetD. equipment23.A. servicesB. processesC. applicationsD. address24.A. iterationB. objectC. interfaceD. activity25.A. applicationB. sessionC. physicalD. transportIt should go without saying that the focus of UML is modeling. However, what that means, exactly, can be an open-ended question. (71) is a means to capture ideas, relationships, decisions, and requirements in a well-defined notation that can be applied to many different domains. Modeling not only means different things to different people, but also it can use different pieces of UML depending on what you are trying to convey. In general, a UML model is made up of one or more (72) . A diagram graphically represents things, and the relationships between these things. These (73) can be representations of real-world objects, pure software constructs, or a description of the behavior of some other objects. It is common for an individual thing to show up on multiple diagrams; each diagram represents a particular interest, or view, of the thing being modeled. UML 2.0 divides diagrams into two categories: structural diagrams and behavioral diagrams. (74) are u sed to capture the physical organization of the things in your system, i.e., how one object relates to another. (75) focus on the behavior of elements in a system. For example, you can use behavioral diagrams to capture requirements, operations, and internal state changes for elements.26.A. ProgrammingB. AnalyzingC. DesigningD. Modeling27.A. viewsB. diagramsC. user viewsD. structure pictures28.A. thingsB. picturesC. languagesD. diagrams29.A. Activity diagramsB. Use-case diagramsC. Structural diagramsD. Behavioral diagrams30.A. Activity diagramsB. Use-case diagramsC. Structural diagramsD. Behavioral diagramsObserve that for the programmer, as for the chef, the urgency of the patron (顾客) may govern the **pletion of the task, but it cannot govern the **pletion. An omelette (煎鸡蛋) , promised in two minutes, may appear to be progressing nicely. But when it has not set in two minutes, the customer has two choices-waits or eats it raw. Software customers have had (71) choices.Now I do not think software (72) have less inherent courage and firmness than chefs, northan other engineering managers. But false (73) to match the patron's desired date is much **mon in our discipline than elsewhere in engineering. It is very (74) to make a vigorous, plausible, and job risking defense of an estimate that is derived by no quantitative method, supported by little data, and certified chiefly by the hunches of the managers.Clearly two solutions are needed. We need to develop and publicize productivity figures, bug-incidence figures, estimating rules, and so on. The whole profession can only profit from (75) such data. Until estimating is on a sounder basis, individual managers will need to stiffen their backbones and defend their estimates with the assurance that their poor hunches are better than wish derived estimates.31.A. noB. the sameC. otherD. lots of32. A.testers B constructors C. managers D. architects33.A. tasksB. jobsC. worksD. scheduling34.A. easyB. difficultC. simpleD. painless35.A. sharingB. excludingC. omittingD. ignoringVirtual reality (or VR (1) ) is kind of a buzzword these days in computer graphics.VR is artificial reality created by a computer that is so enveloping that it is perceived by the mind as being truly real.VR exists in many (2) .A traditional view of virtual reality uses headsets and data gloves.The headset serves as the eyes and ears to your virtual world,projecting sights and sounds generated by **puter.The data glove becomes your hand,enabling you to interact with this (3) world.As you move your head around,**puter will track your motion and display the right image.VR is the most demanding (4) **puter graphics,requiring hardware and software capable of supporting realtime 3D (5) .36.A. for certainB. for any sakeC. for allD. for short37.A. formB. formsC. formatD. shape38.A. dummyB. simulatedC. fictitiousD. invented39.A. AppB. applyC. applicationD. appliance40.A. imageB. FigureC. LogoD. graphicsThe use of computer graphics (1) many diverse fields. Applications (2) from the production of charts and graphs, to the generation of realistic images for television and motion pictures to the (3) design of mechanical parts.To encompass all these uses, we can adopt a simple definition:Computer graphics is concerned with all (4) of using a computer to generate images.We can classify applications of computer graphics into four main areas:Display of information, Design, (5) ,User interfaces.41.A. pervadesB. pervasiveC. perverseD. pervert42.A. scopeB. boundC. rangeD. area43.A. alternantB. interactiveC. alternateD. interactant44.A. shellB. colourC. outlineD. aspects45.A. simulateB. SimulationC. simulatorD. simulacrum。

图像处理_Digital Image Processing, 3rd ed(数字图像处理(第3版)内附图片)

Digital Image Processing, 3rd ed(数字图像处理(第3版)内附图片)数据摘要：DIGITAL IMAGE PROCESSING has been the world's leading textbook in its field for more than 30 years. As in the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992 and 2002 editions by Gonzalez and Woods, this fifth-generation book was prepared with students and instructors in mind. The principal objectives of the book continue to be to provide an introduction to basic concepts and methodologies for digital image processing, and to develop a foundation that can be used as the basis for further study and research in this field. The material is timely, highly readable, and illustrated with numerous examples of practical significance. All mainstream areas of image processing are covered, including 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 otherfeatures. A supplementary instructor's manual is available to instructors who have adopted the book for classroom use. See also a partial list of institutions that use the book.One of the principal reasons this book has been the world leader in its field for more than 30 years is the level of attention we pay to the changing educational needs of our readers. The present edition is based on the most extensive survey we have ever conducted. The survey involved faculty, students, and independent readers of the book in 134 institutions from 32 countries. Many of the following new features are based on the results of that survey.中文关键词：数字图像处理,图像基础,图像在空间和频率域的增强,图像压缩,图像描述,英文关键词：digital image processing,image fundamentals,image compression,image description,数据格式：IMAGE数据用途：数字图像处理数据详细介绍：Digital Image Processing, 3rd editionBasic Information:ISBN number 9780131687288.Publisher: Prentice Hall12 chapters.954 pages.© 2008.DIGITAL IMAGE PROCESSING has been the world's leading textbook in its field for more than 30 years. As in the 1977 and 1987 editions by Gonzalez and Wintz, and the 1992 and 2002 editions by Gonzalez and Woods, this fifth-generation book was prepared with students and instructors in mind. The principal objectives of the book continue to be to provide an introduction to basic concepts and methodologies for digital image processing, and to develop a foundation that can be used as the basis for further study and research in this field. The material is timely, highly readable, and illustrated with numerous examples of practical significance. All mainstream areas of image processing are covered, including image fundamentals, imageenhancement 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. See also a partial list of institutions that use the book.One of the principal reasons this book has been the world leader in its field for more than 30 years is the level of attention we pay to the changing educational needs of our readers. The present edition is based on the most extensive survey we have ever conducted. The survey involved faculty, students, and independent readers of the book in 134 institutions from 32 countries. Many of the following new features are based on the results of that survey.NEW FEATURESA revision of introductory concepts that provides readers with foundation material much earlier in the book than before.A revised and updated discussion of intensity transformation, spatialcorrelation, convolution, and their application to spatial filtering.New discussion of fuzzy sets and their application to image processing.A new chapter on the discrete Fourier transform and frequency domain processing.New coverage of computerized tomography.A revision of the wavelets chapter.A new chapter on data compression, including new compression techniques, digital video compression, standards, and watermarking.New coverage of morphological reconstruction, gray-scale morphology, and advanced morphological algorithms.New coverage of the Marr-Hildreth and Canny edge detection algorithms.Expanded coverage of image thresholding.New examples and illustrations involving over 400 new images and more than 200 new drawings and tables.Expanded homework sets, including over 80 new problems.Updated bibliography.Differences Between the DIP and DIPUM BooksDigital Image Processing is a book on fundamentals.Digital Image Processing Using MATLAB is a book on the software implementation of those fundamentals.The key difference between the books is that Digital Image Processing (DIP) deals primarily with the theoretical foundation of digital image processing, while Digital Image Processing Using MATLAB (DIPUM) is a book whose main focus is the use of MATLAB for image processing. The DIPUM book covers essentially the same topics as DIP, but the theoretical treatment is not as detailed. Some instructors prefer to fill in the theoretical details in class in favor of having available a book with a strong emphasis on implementation.© 2008 by Pearson Education, Inc.Pearson Prentice HallPearson Education, Inc.Upper Saddle River, New Jersey 07458All rights reserved. No part of this book may be reproduced, in any form, or by any means, without permission in writing from the publisher. Pearson Prentice Hall ® is a trademark of Pearson Education, Inc. The authors and publisher of this book have used their best efforts in preparing this book.These efforts include the development, research, and testing of the theories and programs to determine their effectiveness.The authors and publisher make no warranty of any kind, expressed or implied,with regard to these programs or the documentation contained in this book.The authors and publisher shall not be liable in any event for incidental or consequential damages with, or arising outof, the furnishing, performance, or use of these programs. 数据预览：点此下载完整数据集。

应用技术学院-计算机专业英语复习资料

应用技术学院-计算机专业英语复习资料专业英语复习资料一、请写出以下单词的中文意思。

1、floppy disk软盘2、printer打印机3、optical disk光盘4、formatting toolbar 格式工具条5、formula方程式6、relational database关系数据库7、antivirus program抗病毒程序8、fragmented破碎9、user interface用户界面10、bus line总线11、smart card智能卡12、motherboard主板13、digital camera数码相机14、fax machine传真机15、ink-jet printer喷墨打印机16、access time访问时间17、direct access直接存取18、Bluetooth蓝牙19、digital signal数字签名20、protocols协议21、operating system 操作系统22．requirements analysis 需求分析23．network security 网络安全24．data structure 数据结构25．decision support system 决策支持系统26．software crisis 软件危机27．computer virus 电脑病毒28．email attachment 电邮附件29．central processing unit ( CPU )中央处理单元30．ink-jet printer 喷墨打印机31. multimedia 多媒体32. software life cycle软件生命周期33. structured programming 结构化程序34. functional testing 功能测试35. word processor 文字处理36. code windows 代码窗口37. firewall 防火墙38. LAN local area network局域网39. hacker 黑客40. switch 开关41．数据库管理系统database management system42．传输控制协议transmission control protocol43．多文档界面multiple document interface 44．面向对象编程Object-oriented programming 45．只读存储器read-only memory46．数字视频光盘Digital Video Disc47．计算机辅助设计computer aided design48．结构化查询语言Structured Query Language49．通用串行总线Universal Serial Bus50．企业之间的电子商务交易方式EDi二、单项选择题。

webM标准(VP8)

VP8 Data Format and Decoding GuideWebM ProjectGoogle, Inc.3 Corporate Drive, Suite 100Clifton Park, NY 12065 USA/Revised: May 2010LicenseCopyrightChapter 1: IntroductionChapter 2: Format OverviewChapter 3: Compressed Frame TypesChapter 4: Overview of Compressed Data Format Chapter 5: Overview of the Decoding ProcessChapter 6: Description of AlgorithmsChapter 7: Boolean Entropy Decoder7.1 Underlying Theory of Coding7.2 Practical Algorithm Description7.3 Actual ImplementationChapter 8: Compressed Data Components8.1 Tree Coding Implementation8.2 Tree Coding ExampleChapter 9: Frame Header9.1 Uncompressed Data Chunk9.2 Color Space and Pixel Type (Key Frames-only)9.3 Segment-based Adjustments9.4 Loop Filter Type and Levels9.5 Token Partition and Partition Data Offsets9.6 Dequantization Indices9.7 Refresh Golden Frame and AltRef Frame9.8 Refresh Last Frame Buffer9.9 DCT Coefficient Probability Update9.10 Remaining Frame Header Data (non-Key Frame)9.11 Remaining Frame Header Data (Key Frame)Chapter 10: Segment-based Feature Adjustments Chapter 11: Key Frame Macroblock Prediction Records11.1 mb_skip_coeff11.2 Luma Modes11.3 Subblock Mode Contexts11.4 Chroma Modes11.5 Subblock Mode Probability TableChapter 12: Intraframe Prediction12.1 mb_skip_coeff12.2 Chroma Prediction12.3 Luma PredictionChapter 13: DCT Coefficient Decoding13.1 MB Without non-Zero Coefficient Values13.2 Coding of Individual Coefficient Values13.3 Token Probabilities13.4 Token Probability Updates13.5 Default Token Probability TableChapter 14: DCT and WHT Inversion and Macroblock Reconstruction14.1 Dequantization14.2 Inverse Transforms14.3 Implementation of the WHT Inversion14.4 Implementation of the DCT Inversion14.5 Summation of Predictor and ResidueChapter 15: Loop Filter15.1 Filter Geometry and Overall Procedure15.2 Simple Filter15.3 Normal Filter15.4 Calculation of Control ParametersChapter 16: Interframe Macroblock Prediction Records16.1 Intra-Predicted Macroblocks16.2 Inter-Predicted Macroblocks16.3 Mode and Motion Vector Contexts16.4 Split PredictionChapter 17: Motion Vector Decoding17.1 Coding of Each Component17.2 Probability UpdatesChapter 18: Interframe Prediction18.1 Bounds on and Adjustment of Motion Vectors18.2 Prediction Subblocks18.3 Sub-pixel Interpolation18.4 Filter PropertiesChapter 19: ReferencesRevision HistoryREADME for VP8 Bitstream SpecificationHistoryFormatMathMLGoogle hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise implementations of this specification where such license applies only to those patent claims, both currently owned by Google and acquired in the future, licensable by Google that are necessarily infringed by implementation of this specification. If You or your agent or exclusive licensee institute or order or agree to the institution of patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that any implementation of this specification constitutes direct or contributory patent infringement, or inducement of patent infringement, then any rights granted to You under the License for this specification shall terminate as of the date such litigation is filed.This specification is made available under a Creative Commons Attribution 3.0 License.Chapter 1: IntroductionThis document describes the VP8 compressed video data format created by Google On2, together with a discussion of the decoding procedure for this format. It is intended to be used in conjunction with and as a guide to the reference decoder provided by Google On2. If there are any conflicts between this document and the reference source code, the reference source code should be considered correct. The bitstream is defined by the reference source code and not this document.Like many modern video compression schemes, VP8 is based on decomposition of frames into square subblocks of pixels, prediction of such subblocks using previously constructed blocks, and adjustment of such predictions (as well as synthesis of unpredicted blocks) using a discrete cosine transform (hereafter abbreviated as DCT). In one special case, however, VP8 uses a “Walsh-Hadamard” (hereafter abbreviated as WHT) transform instead of a DCT.Roughly speaking, such systems reduce datarate by exploiting the temporal and spatial coherence of most video signals. It is more efficient to specify the location of a visually similar portion of a prior frame than it is to specify pixel values. The frequency segregation provided by the DCT and WHT facilitate the exploitation of both spatial coherence in the original signal and the tolerance of the human visual system to moderate losses of fidelity in the reconstituted signal.VP8 augments these basic concepts with, among other things, sophisticated usage of contextual probabilities. The result is a significant reduction in datarate at a given quality.Unlike some similar schemes (the older MPEG formats, for example), VP8 specifies exact values for reconstructed pixels. Specifically, the specification for the DCT and WHT portions of the reconstruction does not allow for any “drift” caused by truncation of fractions. Rather, the algorithm is specified using fixed-precision integer operations exclusively. This greatly facilitates the verification of the correctness of a decoder implementation as well as avoiding difficult-to-predict visual incongruities between such implementations.It should be remarked that, in a complete video playback system, the displayed frames may or may not be identical to the reconstructed frames. Many systems apply a final level of filtering (commonly referred to as postprocessing) to the reconstructed frames prior to viewing. Such postprocessing has no effect on the decoding and reconstruction of subsequent frames (which are predicted using the completely-specified reconstructed frames) and is beyond the scope of this document. In practice, the nature and extent of this sort of postprocessing is dependent on both the taste of the user and on the computational facilities of the playback environment.Chapter 2: Format OverviewVP8 works exclusively with an 8-bit YUV 4:2:0 image format. In this format, each 8-bit pixel in the two chroma planes (U and V) corresponds positionally to a 2x2 block of 8-bit luma pixels in the Y plane; coordinates of the upper left corner of the Y block are of course exactly twice the coordinates of the corresponding chroma pixels. When we refer to pixels or pixel distances without specifying a plane, we are implicitly referring to the Y plane or to the complete image, both of which have the same (full) resolution.As is usually the case, the pixels are simply a large array of bytes stored in rows from top to bottom, each row being stored from left to right. This “left to right” then “top to bottom” raster-scan order is reflected in the layout of the compressed data as well.Provision has been made for the support of two different YUV color formats in the VP8 bitstream header, however only one format is supported in the first release of VP8.The YUV formats differ in terms of their conversion to and from RGB color space. The first corresponds to the traditional YUV color space similar to the YCrCb color space defined in ITU-R BT.601. The second (currently unsupported) format corresponds to a new YUV color space whose digital conversion to and from RGB can be implemented without multiplications and divides. The VP8 Decoder should decode and pass the information on to the processes that convert the YUV output to RGB color space.Occasionally, at very low datarates, a compression system may decide to reduce the resolution of the input signal to facilitate efficient compression. The VP8 data format supports this via optional upscaling of its internal reconstruction buffer prior to output (this is completely distinct from the optional postprocessing discussed earlier, which has nothing to do with decoding per se). This upsampling restores the video frames to their original resolution. In other words, the compression/decompression system can be viewed as a “black box”, where the input and output is always at a given resolution. The compressor might decide to “cheat” and process the signal at a lower resolution. In that case, the decompressor needs the ability to restore the signal to its original resolution.Internally, VP8 decomposes each output frame into an array of macroblocks. A macroblock is a square array of pixels whose Y dimensions are 16x16 and whose U and V dimensions are 8x8. Macroblock-level data in a compressed frame occurs (and must be processed) in a raster order similar to that of the pixels comprising the frame.Macroblocks are further decomposed into 4x4 subblocks. Every macroblock has 16 Y subblocks, 4 U subblocks, and 4 V subblocks. Any subblock-level data (and processing of such data) again occurs in raster order, this time in raster order within the containing macroblock.As discussed in further detail below, data can be specified at the levels of both macroblocks and their subblocks.Pixels are always treated, at a minimum, at the level of subblocks, which may be thought of as the “atoms” of the VP8 algorithm. In particular, the 2x2 chroma blocks corresponding to 4x4 Y subblocks are never treated explicitly in the data format or in the algorithm specification.The DCT and WHT always operate at a 4x4 resolution. The DCT is used for the 16Y, 4U and 4V subblocks. The WHT is used (with some but not all prediction modes) to encode a 4x4 array comprising the average intensities of the 16 Y subblocks of a macroblock. These average intensities are, up to a constantnormalization factor, nothing more that the zero th DCT coefficients of the Y subblocks. This “higher-level”WHT is a substitute for the explicit specification of those coefficients, in exactly the same way as the DCT of a subblock substitutes for the specification of the pixel values comprising the subblock. We consider this 4x4 array as a second-order subblock called Y2, and think of a macroblock as containing 24 “real” subblocks and, sometimes, a 25th “virtual” subblock. This is dealt with further in Chapter 13.The frame layout used by the reference decoder may be found in the file yv12config.h.Chapter 3: Compressed Frame TypesThere are only two types of frames in VP8.Intraframes(also called key frames and, in MPEG terminology,I-frames) are decoded without reference to any other frame in a sequence, that is, the decompressor reconstructs such frames beginning from its “default” state. Key frames provide random access (or seeking) points in a video stream.Interframes(also called prediction frames and, in MPEG terminology,P-frames) are encoded with reference to prior frames, specifically all prior frames up to and including the most recent key frame. Generally speaking, the correct decoding of an interframe depends on the correct decoding of the most recent key frame and all ensuing frames. Consequently, the decoding algorithm is not tolerant of dropped frames: In an environment in which frames may be dropped or corrupted, correct decoding will not be possible until a key frame is correctly received.In contrast to MPEG, there is no use of bidirectional prediction. No frame is predicted using frames temporally subsequent to it; there is no analog to an MPEG B-frame.Secondly, VP8 augments these notions with that of alternate prediction frames, called golden frames and altref frames(alternative reference frames). Blocks in an interframe may be predicted using blocks in the immediately previous frame as well as the most recent golden frame or altref frame. Every key frame is automatically golden and altref, and any interframe may optionally replace the most recent golden or altref frame.Golden frames and altref frames may also be used to partially overcome the intolerance to dropped frames discussed above: If a compressor is configured to code golden frames only with reference to the prior golden frame (and key frame) then the “substream” of key and golden frames may be decoded regardless of loss of other interframes. Roughly speaking, the implementation requires (on the compressor side) that golden frames subsume and recode any context updates effected by the intervening interframes. A typical application of this approach is video conferencing, in which retransmission of a prior golden frame and/or a delay in playback until receipt of the next golden frame is preferable to a larger retransmit and/or delay until the next key frame.Chapter 4: Overview of Compressed Data FormatThe input to a VP8 decoder is a sequence of compressed frames whose order matches their order in time. Issues such as the duration of frames, the corresponding audio, and synchronization are generally provided by the playback environment and are irrelevant to the decoding process itself, however, to aid in fast seeking a start code is included in the header of each key frame.The decoder is simply presented with a sequence of compressed frames and produces a sequence of decompressed (reconstructed) YUV frames corresponding to the input sequence. As stated in the introduction, the exact pixel values in the reconstructed frame are part of VP8’s specification. This document specifies the layout of the compressed frames and gives unambiguous algorithms for the correct production of reconstructed frames.The first frame presented to the decompressor is of course a key frame. This may be followed by any number of interframes; the correct reconstruction of each frame depends on all prior frames up to the key frame. The next key frame restarts this process: The decompressor resets to its default initial condition upon reception of a key frame and the decoding of a key frame (and its ensuing interframes) is completely independent of any prior decoding.At the highest level, every compressed frame has three or more pieces. It begins with an uncompressed data chunk comprising 10 bytes in the case of key frames and 3-bytes for inter frames. This is followed by two or more blocks of compressed data (called partitions). These compressed data partitions begin and end on byte boundaries.The first compressed partition has two subsections:1.Header information that applies to the frame as a whole.2.Per-macroblock information specifying how each macroblock is predicted from the already-reconstructed data that is available to the decompressor.As stated above, the macroblock-level information occurs in raster-scan order.The rest of the partitions contain, for each block, the DCT/WHT coefficients (quantized and logically compressed) of the residue signal to be added to the predicted block values. It typically accounts for roughly 70% of the overall datarate. VP8 supports packing the compressed DCT/WHT coefficients’ data from macroblock rows into separate partitions. If there is more than one partition for these coefficients, the sizes of the partitions — except the last partition — in bytes are also present in the bitstream right after the above first partition. Each of the sizes is a 3-byte data item written in little endian format. These sizes provide the decoder direct access to all DCT/WHT coefficient partitions, which enables parallel processing of the coefficients in a decoder.The separate partitioning of the prediction data and coefficient data also allows flexibility in the implementation of a decompressor: An implementation may decode and store the prediction information for the whole frame and then decode, transform, and add the residue signal to the entire frame, or it may simultaneously decode both partitions, calculating prediction information and adding in the residue signal for each block in order. The length field in the frame tag, which allows decoding of the second partition to begin before the first partition has been completely decoded, is necessary for the second “block-at-a-time” decoder implementation.VP8 Data Format and Decoding Guide Chapter 4: Overview of Compressed Data FormatAll partitions are decoded using separate instances of the boolean entropy decoder described in Chapter 7.Although some of the data represented within the partitions is conceptually “flat” (a bit is just a bit with noprobabilistic expectation one way or the other), because of the way such coders work, there is never a direct correspondence between a “conceptual bit” and an actual physical bit in the compressed data partitions. Only in the 3 or 10 byte uncompressed chunk described above is there such a physical correspondence.A related matter, which is true for most lossless compression formats, is that seeking within a partition is notsupported. The data must be decompressed and processed (or at least stored) in the order in which it occurs in the partition.While this document specifies the ordering of the partition data correctly, the details and semantics of thisdata are discussed in a more logical fashion to facilitate comprehension. For example, the frame headercontains updates to many probability tables used in decoding per-macroblock data. The latter is oftendescribed before the layouts of the probabilities and their updates, even though this is the opposite of theirorder in the bitstream.Chapter 5: Overview of the Decoding ProcessA VP8 decoder needs to maintain four YUV frame buffers whose resolutions are at least equal to that of the encoded image. These buffers hold the current frame being reconstructed, the immediately previous reconstructed frame, the most recent golden frame, and the most recent altref frame.Most implementations will wish to “pad” these buffers with “invisible” pixels that extend a moderate number of pixels beyond all four edges of the visible image. This simplifies interframe prediction by allowing all (or most) prediction blocks — which are not guaranteed to lie within the visible area of a prior frame — to address usable image data.Regardless of the amount of padding chosen, the invisible rows above (below) the image are filled with copies of the top (bottom) row of the image; the invisible columns to the left (right) of the image are filled with copies of the leftmost (rightmost) visible row; and the four invisible corners are filled with copies of the corresponding visible corner pixels. The use of these prediction buffers (and suggested sizes for the halo) will be elaborated on in the discussion of motion vectors, interframe prediction, and sub-pixel interpolation later in this document.As will be seen in the description of the frame header, the image dimensions are specified (and can change) with every key frame. These buffers (and any other data structures whose size depends on the size of the image) should be allocated (or re-allocated) immediately after the dimensions are decoded.Leaving most of the details for later elaboration, the following is an outline the decoding process.First, the frame header (beginning of the first data partition) is decoded. Altering or augmenting the maintained state of the decoder, this provides the context in which the per-macroblock data can be interpreted.The macroblock data occurs (and must be processed) in raster-scan order. This data comes in two or more parts. The first (prediction or mode) part comes in the remainder of the first data partition. The other parts comprise the data partition(s) for the DCT/WHT coefficients of the residue signal. For each macroblock, the prediction data must be processed before the residue.Each macroblock is predicted using one (and only one) of four possible frames. All macroblocks in a key frame, and all intra-coded macroblocks in an interframe, are predicted using the already-decoded macroblocks in the current frame. Macroblocks in an interframe may also be predicted using the previous frame, the golden frame or the altref frame. Such macroblocks are said to be inter-coded.The purpose of prediction is to use already-constructed image data to approximate the portion of the original image being reconstructed. The effect of any of the prediction modes is then to write a macroblock-sized prediction buffer containing this approximation.Regardless of the prediction method, the residue DCT signal is decoded, dequantized, reverse-transformed, and added to the prediction buffer to produce the (almost final) reconstruction value of the macroblock, which is stored in the correct position of the current frame buffer.The residue signal consists of 24 (sixteen Y, four U, and four V) 4x4 quantized and losslessly-compressed DCT transforms approximating the difference between the original macroblock in the uncompressed sourceand the prediction buffer. For most prediction modes, the zero th coefficients of the sixteen Y subblocks are expressed via a 25th WHT of the second-order virtual Y2 subblock discussed above.Intra-prediction exploits the spatial coherence of frames. The 16x16 luma (Y) and 8x8 chroma (UV) components are predicted independently of each other using one of four simple means of pixel propagation, starting from the already-reconstructed (16-pixel long luma, 8-pixel long chroma) row above and column to the left of the current macroblock. The four methods are:1.Copying the row from above throughout the prediction buffer.2.Copying the column from left throughout the prediction buffer.3.Copying the average value of the row and column throughout the prediction buffer.4.Extrapolation from the row and column using the (fixed) second difference (horizontal and vertical)from the upper left corner.Additionally, the sixteen Y subblocks may be predicted independently of each other using one of ten different modes, four of which are 4x4 analogs of those described above, augmented with six “diagonal” prediction methods. There are two types of predictions, one intra and one prediction (among all the modes), for which the residue signal does not use the Y2 block to encode the DC portion of the sixteen 4x4 Y subblock DCTs. This “independent Y subblock” mode has no effect on the 8x8 chroma prediction.Inter-prediction exploits the temporal coherence between nearby frames. Except for the choice of the prediction frame itself, there is no difference between inter-prediction based on the previous frame and that based on the golden frame or altref frame.Inter-prediction is conceptually very simple. While, for reasons of efficiency, there are several methods of encoding the relationship between the current macroblock and corresponding sections of the prediction frame, ultimately each of the sixteen Y subblocks is related to a 4x4 subblock of the prediction frame, whose position in that frame differs from the current subblock position by a (usually small) displacement. These two-dimensional displacements are called motion vectors.The motion vectors used by VP8 have quarter-pixel precision. Prediction of a subblock using a motion vector that happens to have integer (whole number) components is very easy: the 4x4 block of pixels from the displaced block in the previous, golden, or altref frame are simply copied into the correct position of the current macroblock’s prediction buffer.Fractional displacements are conceptually and implementationally more complex. They require the inference (or synthesis) of sample values that, strictly speaking, do not exist. This is one of the most basic problems in signal processing and readers conversant with that subject will see that the approach taken by VP8 provides a good balance of robustness, accuracy, and efficiency.Leaving the details for the implementation discussion below, the pixel interpolation is calculated by applying a kernel filter (using reasonable-precision integer math) three pixels on either side, both horizontally and vertically, of the pixel to be synthesized. The resulting 4x4 block of synthetic pixels is then copied into position exactly as in the case of integer displacements.Each of the eight chroma subblocks is handled similarly. Their motion vectors are never specified explicitly; instead, the motion vector for each chroma subblock is calculated by averaging the vectors of the four Y subblocks that occupy the same area of the frame. Since chroma pixels have twice the diameter (and four times the area) of luma pixels, the calculated chroma motion vectors have 1/8 pixel resolution, but theprocedure for copying or generating pixels for each subblock is essentially identical to that done in the luma plane.After all the macroblocks have been generated (predicted and corrected with the DCT/WHT residue), a filtering step (the loop filter) is applied to the entire frame. The purpose of the loop filter is to reduce blocking artifacts at the boundaries between macroblocks and between subblocks of the macroblocks. The term loop filter is used because this filter is part of the “coding loop,” that is, it affects the reconstructed frame buffers that are used to predict ensuing frames. This is distinguished from the postprocessing filters discussed earlier which affect only the viewed video and do not “feed into” subsequent frames.Next, if signaled in the data, the current frame (or individual macroblocks within the current frame) may replace the golden frame prediction buffer and/or the altref frame buffer.The halos of the frame buffers are next filled as specified above. Finally, at least as far as decoding is concerned, the (references to) the “current” and “last” frame buffers should be exchanged in preparation for the next frame.Various processes may be required (or desired) before viewing the generated frame. As discussed in the frame dimension information below, truncation and/or upscaling of the frame may be required. Some playback systems may require a different frame format (RGB, YUY2, etc.). Finally, as mentioned in the introduction, further postprocessing or filtering of the image prior to viewing may be desired. Since the primary purpose of this document is a decoding specification, the postprocessing is not specified in this document.While the basic ideas of prediction and correction used by VP8 are straightforward, many of the details are quite complex. The management of probabilities is particularly elaborate. Not only do the various modes of intra-prediction and motion vector specification have associated probabilities but they, together with the coding of DCT coefficients and motion vectors, often base these probabilities on a variety of contextual information (calculated from what has been decoded so far), as well as on explicit modification via the frame header.The “top-level” of decoding and frame reconstruction is implemented in the reference decoder filesonyxd_if.c and decodframe.c.This concludes our summary of decoding and reconstruction; we continue by discussing the individual aspects in more depth.A reasonable “divide and conquer” approach to implementation of a decoder is to begin by decoding streams composed exclusively of key frames. After that works reliably, interframe handling can be added more easily than if complete functionality were attempted immediately. In accordance with this, we first discuss components needed to decode key frames (most of which are also used in the decoding of interframes) and conclude with topics exclusive to interframes.。

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Web-based document image processingFrank L. Walker and George R. ThomaLister Hill National Center for Biomedical CommunicationsNational Library of MedicineBethesda, Maryland 20894ABSTRACTIncreasing numbers of research libraries are turning to the Internet for electronic interlibrary loan and for document delivery to patrons. This has been made possible through the widespread adoption of software such as Ariel and DocView. Ariel, a product of the Research Libraries Group, converts paper-based documents to monochrome bitmapped images, and delivers them over the Internet. The National Library of Medicine’s DocView is primarily designed for library patrons to receive, display and manage documents received from Ariel systems. While libraries and their patrons are beginning to reap the benefits of this new technology, barriers exist, e.g., differences in image file format, that lead to difficulties in the use of library document information. To research how to overcome such barriers, the Communications Engineering Branch of the Lister Hill National Center for Biomedical Communications, an R&D division of NLM, has developed a web site called the DocMorph Server. This is part of an ongoing intramural R&D program in document imaging that has spanned many aspects of electronic document conversion and preservation, Internet document transmission and document usage. The DocMorph Server web site is designed to fill two roles. First, in a role that will benefit both libraries and their patrons, it allows Internet users to upload scanned image files for conversion to alternative formats, thereby enabling wider delivery and easier usage of library document information. Second, the DocMorph Server provides the design team an active test bed for evaluating the effectiveness and utility of new document image processing algorithms and functions, so that they may be evaluated for possible inclusion in other image processing software products being developed at NLM or elsewhere. This paper describes the design of the prototype DocMorph Server and the image processing functions being implemented on it.Keywords: DocMorph Server, Image Processing, World Wide Web, TIFF, PDF, OCR, DocView, NLM, Ariel, Internet, Speech Synthesis1. INTRODUCTIONDocument delivery by libraries and information service providers has evolved over the past two decades. Interlibrary loan has traditionally meant photocopies of journal articles being mailed to other libraries. While photocopies are still mailed to requesters, document delivery libraries have since added facsimile transmission, and more recently, Internet document delivery. The 1990s decade has seen the arrival of Internet delivery of library documents, especially with the widespread use of the Ariel TM system developed and distributed by Research Libraries Group.1,2 Ariel has enabled several thousand libraries to do interlibrary loan electronically via the Internet. It is a technology that is faster than mail, more reliable than fax, and offers higher resolution images than possible through conventional fax. While libraries used Ariel in the first half of this decade for interlibrary loan, the second half has seen more use of the Internet for document delivery to the patron’s desktop computer. DocView, a software product developed at the National Library of Medicine, helps librarians achieve the goal of delivering library documents over the Internet to the patron’s desktop.3,4Running on all Windows TM operating systems, DocView is client software that enables a library patron to receive documents sent by a library’s Ariel system. DocView’s compatibility with Ariel enables a library or document supplier to use Ariel to scan a printed document and send the resulting images directly to a patron’s computer running DocView. Ariel’s scanning process produces a file of bitmapped images, which are sent via File Transfer Protocol (FTP)5 or Multipurpose Mime Email Extensions (MIME) email.6DocView is capable of displaying monochrome bitmapped images in either the Group on Electronic Document Interchange7 (GEDI) file format used by Ariel systems, or in the Tagged Image File Format8 (TIFF). Besides displaying the received images, DocView permits the user to zoom, scroll, pan and rotate them. In addition, a user may “bookmark” pages for easy browsing or printing, and images may be copied for insertion in word processing documents. DocView also allows the user to file and organize the received documents through a built-in document management system. Finally, DocView permits the user to forward documents over the Internet to others, using either FTP or MIME email. This last feature might be useful in an interlibrary loan service where the library receiving the document may, after completing any necessary bookkeeping, forward it to a patron.An extensive period of beta testing that lasted 2½ years confirmed that a large majority of users found that DocView had improved the delivery of documents from their libraries.9 DocView was released in January 1998 and is freely available. Since DocView’s release, over 5,000 registered users in more than 90 countries have downloaded it. A web site established to distribute DocView includes an extensive user manual, a report on the DocView beta test, and published papers related to DocView. The software can beWhile libraries and their patrons are benefiting from Internet document delivery through the use of Ariel and DocView, there are still problems to be solved. These include the problems of delivering electronic documents to a wide variety of user platforms. Also, document file format often becomes an issue because library patrons often do not have the requisite software for handling bitmapped image documents. To research how to overcome potential problems such as these, the Communications Engineering Branch of the Lister Hill National Center for Biomedical Communications, an R&D division of NLM, has developed a web site termed the DocMorph Server. This is part of an ongoing R&D program in document imaging that has spanned many aspects of electronic document conversion and preservation, Internet document transmission and document usage.2. THE DOCMORPH SERVERThe DocMorph Server is intended to serve two purposes:1. As a resource for researching document-imaging techniques and algorithms that could find applicationin future versions of the DocView software and other software being developed at NLM.2. To provide platform-independent remote document image processing capabilities to the biomedicallibrary community via the World Wide Web.A user may upload library document images from anywhere via the World Wide Web to the DocMorph Server to be processed. After the DocMorph Server has finished processing the images, it returns the results to the user. During the DocView beta test, some testers indicated a need for several types of document conversion. Among these were conversions from multipage TIFF to single page TIFF, from single page TIFF to multipage TIFF, or from TIFF to Adobe PDF files. There was also interest in searching documents. Files produced by the Ariel system are strictly bitmapped images; they are not text-searchable unless converted to text by optical character recognition (OCR). It is possible to include this type of document conversion in the DocMorph Server. It is also possible to include other image processing algorithms for cleaning up artifacts created by the scanning process, such as removal of image skew or borders, which would improve the accuracy of OCR conversion.Some of these document image-processing techniques and algorithms are candidates for inclusion in future versions of the DocView software. By evaluating some of the proposed DocView capabilities first on the DocMorph Server, it is possible to bypass the costly time delays associated with beta-testing software such as DocView. With a built-in questionnaire and email feedback capability built directly into the web pages of the DocMorph Server, there is fast user feedback. Also, complete statistics on usage of the server are kept, including which functions are used, who uses them, and the processing time for each algorithm.The first practical use of the DocMorph Server is in providing alternative document formats for users. One of the problems that document delivery librarians often experience, particularly on university campuses, is that of delivering documents to a diverse group of patrons. Convenient Internet delivery of library documents is possible when all patrons have the requisite document viewing software. However, not everybody runs Windows on his or her computer, and document delivery librarians often have to serve patrons who have UNIX systems and Macintosh computers. If a library patron receives an Ariel document, but does not have the proper software for viewing it (which may be the case for non-Windows computers), then the DocMorph Server provides a solution. The patron may send the received file to the DocMorph Server to have it converted to a suitable alternative format. Document delivery librarians may also use the Server to change a document to another format prior to delivering it to a patron.3. DOCMORPH SERVER FUNCTIONSThe initial prototype DocMorph Server that was first made available to the public in April 1999 had three document conversion functions.• Concatenate one or more TIFF files to create a PDF file. This function concatenates one or more Ariel (GEDI) files or TIFF files to create a single PDF file. This function is useful for users who wish to use Adobe Acrobat Reader™ to view a file received from a library. Librarians may also use this function to convert Ariel documents to PDF prior to delivery to patrons.• Concatenate TIFF files into one multipage TIFF file. This function is used to concatenate multipage TIFF image files to form one multipage TIFF file that contains all images. This is useful for creating new single file documents. For instance, single file pages can be concatenated to form a chapter, and chapters can be concatenated to create a book.• Split a multipage TIFF file into individual image files. This function is useful for document editing, especially when combined with the previous function. By using the two functions together, a user will be able to replace, delete or insert pages in a multipage TIFF file.By the end of the first six months of use, more than 600 users registered to use the DocMorph Server. Of these, more than 300 used the system at one time or another over this period. They submitted more than 1,600 jobs containing more than 17,000 images to DocMorph. Of these, 1,400 ran successfully while 200 failed. The failures were virtually all due to file formats that were not supported by DocMorph such as GIF, JPEG and text. Of the 1,400 successful jobs, 3% were concatenation of TIFF files, 7% were splitting TIFF files, and 90% were conversions of TIFF to PDF.A fourth conversion function was added to DocMorph in October 1999 when the system was redesigned to support compute-intensive image processing:• Computer-assisted reading. This allows conversion of scanned images to synthesized speech.With this function, a user can scan a printed document, submit it to DocMorph, and receive in return an audio “document” in the form of a web page that reads the material out loud on the user’s computer. This capability is intended to support an important minority in our population that often has difficulty in reading printed literature: the blind, visually impaired and physically handicapped. It has been estimated that in 1990 there were 550,000 Americans who were blind in both eyes and an additional 7.6 million who had vision impairments.10 Blindness is defined as best-corrected visual acuity of 20/200 or worse in the better eye; visual impairment as best-corrected visual acuity of worse than 20/40 but better than 20/200 in the better eye. The computer-assisted reading function will enable these types of people to have easier access to literature. The user interface provided by DocMorph allows the user to browse the resulting audio document by using only two keyboard keys. Functionality included for advanced users permits the user to search for audio text words and to randomly access any page within the audio document.In addition to computer-assisted reading, the TIFF to PDF algorithm was enhanced with the inclusion of automatic page rotation, a feature needed to overcome certain problems with scanning. A number of scanners on the market are not well suited for scanning bound volumes because of the way the scanner cover is hinged. For books or journals, a scanner should have its cover hinged along the short paper edge (8.5”) rather than along the long edge (11”). This would permit the operator to easily move the book leftand right over the scanning area. If, however, the cover is hinged along the long edge of the paper, this often prevents both left and right pages of the book from being scanned with the same orientation. The result is that the operator may need to turn the document upside down every other page. The Ariel software does not provide any means for rotating the pages to ensure that they are all right side up. As a result, the electronic file received by the patron is difficult to use on a computer screen because every other page needs to be rotated 1800. Like Ariel, the Adobe Acrobat Reader does not provide a function for rotating pages. If such a file is received, users have to print the document first, and rotate the pages manually. This has become an inconvenience for both document delivery librarians and their patrons, as evidenced by comments received from several DocMorph users. Our new TIFF to PDF algorithm allows the user to choose to have all pages in the document to be upright. During conversion to PDF, the software uses OCR to see if a page is upside down. Those pages found to be upside down are automatically rotated so that they are correctly oriented for reading.4. DOCMORPH SERVER: SINGLE COMPUTER DESIGN12 system. PubMed is an example of an application server that provides complex searching functionality for users via the Internet. The DocMorph Server is an application server with a three-tier design. When its first version was introduced in April 1999, the DocMorph Server’s single computer architecture, shown in Figure 1, consisted of a web server, a conversion server, and a database.DocMorph ServerFigure 1. DocMorph Server Single Computer ConfigurationThe prototype DocMorph Server is designed to run on a computer hosting Microsoft’s Windows NT Server operating system. Windows NT Server provides the Internet Information Server (IIS), a highly capable web server that permits processing of HTTP requests using DocMorph’s Visual Basic WebClasses. WebClasses are a Windows-based alternative to CGI scripting that is commonly found on UNIX-based web servers. They use compiled Visual Basic to quickly process requests sent to the web server and return HTML-based web pages. In addition to code written in Visual Basic, other modules created for DocMorph are written in C, C++, assembly language and JavaScript.A user of the DocMorph Server employs a web browser to access the DocMorph Server home page, which allows the user to register, log in and use the server. Once a registered user has logged in, the web browser displays four functions that permit document conversion. For each function the user needs to select one or more files to upload to the DocMorph Server. File uploading takes place using HTTP communication protocols. The Server software intercepts the uploaded file(s) and stores them temporarily on its local hard disk drive. Then it makes an entry in its database to denote the location of the file(s) to be converted, and the nature of the conversion.A Conversion Server written in C++ periodically queries the database for work, and upon finding work to be done, the Conversion Server converts the file(s) to the requested form, and then updates the database. Meanwhile, the user’s web browser queries the DocMorph Server every twenty seconds to keep track of the document conversion process. Each update request requires the WebClass running on the Web Server to query the database for job completion. When the database shows the job is completed, an HTML page is dynamically created to denote job status and allows the user to download the converted file(s). Two hours after a user has downloaded converted files, the Conversion Server removes the converted files from the computer to free up disk space. The database is used to ensure proper job selection and timing, and it also keeps track of user statistics and feedback.5. DOCMORPH SERVER: MULTI-COMPUTER DESIGNInitial testing and use of the DocMorph Server revealed that it ran satisfactorily for a small number of simultaneous users. On a 400 MHz processor, the average processing time for each of the initial three document conversion algorithms was 10 seconds. This included the built-in overhead for cycling between jobs, picking the next conversion job, running the job, and updating the database. Five potential bottlenecks were identified in the system design:1. Communication speed. Users with relatively fast Internet connections (56K and above) expressedthe highest satisfaction with DocMorph. Speeds lower than 33K were generally unsatisfactory for uploading and downloading document images.2. File size. Scanned document images produce large files, even with Group 4 compression11. At300 dpi resolution a typical 8.5x11 inch page can produce 100,000 bytes of compressed data, so that a typical 10-page journal article will average one megabyte.3. Database overhead. The DocMorph Server uses a Microsoft Access database. This typically limitssimultaneous access to 20 people with satisfactory response time.4. Processing time. While DocMorph’s file conversion algorithms are fast, any compute-intensivetask such as optical character recognition would slow response time considerably. The average measured time to convert a typical 300 dpi biomedical journal image to text using OCR ranges from 10 to 12 seconds on a 500 MHz computer. Because the OCR process consumes virtually all the CPU time, other processes such as web page serving and database access proceed very slowly if all run on the same CPU.5. Serving web pages. A typical Microsoft IIS web server can handle several hundred simultaneoususers, but it cannot handle thousands simultaneously.To meet our objective of implementing optical character recognition in DocMorph, it was not possible to build it into the initial single computer architecture. This is because OCR is compute-intensive and greatly slows down all the other tasks that may be running on the computer. It was decided to create a multi-computer architecture to alleviate some of the problems identified in our testing of the single computer design. The multi-computer design lessens communication slowdowns due to simultaneous file upload/download of two or more users. It also provides a web server on each computer to help spread the load created by a large number of users. Database overhead is reduced because the database is split among all computers. Finally, processing time of compute-intensive tasks such as OCR is offloaded onto separate computers.Figure 2 illustrates the multi-computer DocMorph Server architecture that was introduced in October 1999. It consists of identical 500 MHz Windows NT servers, each running the IIS web server software.DocMorph2Figure 2. DocMorph Server Multi-Computer ConfigurationThe DocMorph Server design permits up to ten computers to run together. While the initial configuration has three computers, additional computers may be added to accommodate increasing usage. The ten computers are designated DocMorph, Docmorph1, DocMorph2, …, DocMorph9. If there is only one computer required in the architecture, the design is nearly the same as the original single computer design. The chief difference in the multi-computer architecture is the Job Switch, which controls job routing. Each time a new computer is added, all computers must be rebooted, and they begin running automatically in the newly reconfigured system.All users initially point their web browsers to the main computer, DocMorph, which contains the Job Switch and Long Term Database. Depending on the job requested by the user, the Job Switch routes the user to a computer that handles the specific job. For example, a computer might be dedicated to the OCR function, and another to converting TIFF images to PDF. The Long Term Database on DocMorph keeps track of users and a complete history of all jobs submitted to all computers within the system. The Short Term Databases on the remaining DocMorph computers maintain data only on the specific jobs submitted to each of those computers. Each Conversion Server updates not only its Short Term Database, but also the Long Term Database on the main DocMorph computer. This way each secondary computer keeps track of its own jobs, while permitting overall system status to be obtained from the single DocMorph computer.As illustrated in Figure 2, the DocMorph computer handles jobs 1, 2 and 3, corresponding to the three TIFF file conversion utilities. The two other computers, DocMorph1 and DocMorph2, each process only Job 4, which (as an example) is the process for converting scanned images to synthesized speech. The Job Switch in DocMorph will route all Job 1, 2 and 3 requests only to DocMorph, and all Job 4 requests to either DocMorph1 or DocMorph2. While Jobs 1, 2 and 3 each consume comparatively little CPU time, Job 4, the OCR process, is compute-intensive, requiring a separate CPU. Each computer handles its jobs in a First-In-First-Out queue that handles jobs in the order in which they arrive. As shown in Figure 3, the load-balancing Job Switch not only routes a job to the correct computer, but it ensures that jobs are distributed evenly, so that no one computer gets too many jobs. In this figure, if a newly arrived job is Job 4, the Job Switch will route this job to DocMorph 1, since it is processing one less job of type 4 than DocMorph2.Figure 3. Load-Balancing Job SwitchAny of the DocMorph Server computers can run any job. It is up to the system administrator to configure them to maintain an even balance of computer processing, web page serving, database accessing and communications loading. The Long Term Database on the DocMorph computer maintains the entire system configuration that determines the specific jobs that will be run on each computer.The DocMorph Server design ensures that each computer in the system can withstand outages. Although short-term power failures are adequately protected using uninterruptible power supplies for each DocMorph Server computer, if that fails and the computer goes down for a long period, then it is designed to start up automatically upon power-up. This is accomplished through an NT Service module that runs upon startup. The NT Service module reads the configuration file for the computer on which it is running, updates the system registry, and automatically starts the Conversion Server. Once the Conversion Server on each computer runs, it checks the Long Term Database on the DocMorph computer to determine the types of jobs it will run. It also updates the Long Term Database once every five minutes to let the Job Switch know that the computer on which it is running is operational. As part of the job allocation algorithm, the Job Switch checks to make sure that the computer to which the job will be routed is functional. It does this by checking the Long Term Database to ensure that the selected computer has accessed it within the past five minutes.The DocMorph system is also designed for remote administration. For instance, it contains a built-in function available through the WWW that provides an overall status of the entire multi-computer system. The status indicates the number of jobs currently being processed by each computer, the number of jobs processed within the past two hours, the number of users currently logged into the system, and an analysis of jobs that have failed. The system administrator user interface also permits the entire system to be brought down or up remotely over the WWW, and the system to be rebooted in the same manner.An important feature of the DocMorph system architecture is maintaining state when switching a user from one computer to another. Because the Job Switch can route a user from one computer to another and then back again, it is necessary to have a mechanism for keeping track of each user and the jobs being submitted to the overall system. This is done through the use of a cookie, a small file that can be transmitted from a web server to the user’s hard disk drive via the web browser. As illustrated in Figure 4, when the user first logs into DocMorph, a cookie containing a unique identifier created using a random number generator is sent to the user’s computer.…CookieFigure 4. State Maintenance using CookiesAt the same time DocMorph sends the cookie to the user, it stores the unique identifier corresponding to the user in the Long Term Database. If the Job Switch routes the user to another computer, say DocMorph2, the user’s browser sends the unique identifier from the cookie to the new computer, which in turn stores the user information in its Short Term Database. In this manner all computers to which the user is routed can keep track of that user. Each computer uses the cookie information to update system usage properly in the Long Term Database on DocMorph.6. DOCMORPH SERVER EVALUATIONWhile intensive testing of the original DocMorph Server design has led to the current multi-computer architecture, evaluation of the new system has just begun. As mentioned above, it contains new and useful functions: automatic rotation of upside down images during file conversion, and computer-assisted reading. These functions will be evaluated for their usefulness and user acceptance. Based on user feedback, algorithms will be enhanced to remove bugs and overcome any performance problems.A major innovation in the multi-computer architecture is computer-assisted reading, which converts document images to synthesized speech. To evaluate this new functionality, users who may have problems reading due to poor vision will be invited to try the system. The two areas of primary investigation will be the user interface and the reliability of conversion. There are two potential sources of error in the conversion: OCR and speech synthesis. The accuracy of OCR depends on several factors, including scan resolution, font types and sizes, text attributes (italics, bolding, etc.) and overall image quality. Speech synthesis is highly dependent on the quality of the OCR conversion as well as the language. Because the initial speech engine being used in the system processes only English, all job submissions are required to be English in origin. A user survey on the system will allow users to submit comments on their experiences with this new capability.7. SUMMARYThe prototype DocMorph Server is based on a multi-computer architecture, and provides functions for document image processing. It is publicly available through the World Wide Web. By using a web browser, users may upload image files to DocMorph from any place on the Internet. DocMorph returns results via the web, which users may store on their computers. Current functionality includes file conversion such as TIFF to PDF, automatic detection and rotation of upside down document images, and conversion of scanned images of printed literature to synthesized speech. While the DocMorph Server is designed to provide a useful service to the public, it is also an image processing test platform that its designers are using to evaluate its algorithms and techniques for possible inclusion in other image processing software products being developed at NLM.8. REFERENCES1. Berger, M.A., “Ariel Document Delivery and the Small Academic Library,” College & UndergraduateLibraries, Vol. 3(2). The Haworth Press, 1996; 49-56.2. The World Wide Web address for Research Libraries Group is located at this URL:.3. Walker FL, Thoma GR, “DocView: Providing Access to Printed Literature through the Internet,”Proceedings IOLS’95. Medford NJ: Learned Information, 1995; 165-173.4. Walker FL, Thoma GR, “Internet Document Access and Delivery,” Proceedings IOLS’96. MedfordNJ: Learned Information, 1996; 107-116.5.6.。