中英文文献翻译-原型基于颜色的图像检索与MATLAB

英文原文

Prototyping Color-based Image Retrieval with MATLAB

Abstrac

Content-based retrieval of (image) databases has become more popular than before. Algorithm develop-ment for this purpose requires testing/simulation tools,but there are no suitable commercial tools on the market.

A simulation environment for retrieving images from database according histogram similarities is presented in this paper. This environment allows the use of different color spaces and numbers of bins. The algorithms are implemented with MA TLAB. Each color system has its own m-files.

The phases of the software building process are pre-sented from system design to graphical user interface (GUI). The functionality is described with snapshots of GUI.

1. Introduction

Nowadays there are thousands or hundreds of thousands of digital images in an image database. If the user wants to find a suitable image for his/her purposes, he/she has to go through the database until the correct image has been found or use a reference book or some “intelligent” program. Video on demand (V oD) services also requires an intelligent search system for end-users. V oD systems’ search methods differ slightly from image database’s methods.

A reference book is a suitable option, if the images are arranged with a useful method, for example: 1)categories: animals, flags, etc, 2) names (requires a good naming technique) or 3) dates. An experienced user can use these systems as well as textual searches (keywords have to be inserted in a database) efficiently. There are situations when a multi-language system has to be used. There a language independent search system’s best properties can be utilized. A tool which is based on the images’ properties can be madelanguage independent. These properties can be for example color, shape, texture, spatial location of shape etc.

In the MuVi-project [1] this kind of tool is under construction. It will cover the properties presented above.Research work on content-based image retrieval has been done in [2 – 6]. The system, which is present ed in this paper, is a simulation environment, where MuVi’s color content based retrieval has been developed and tested.

2. System development

MATLAB is an efficient program for vector and matrix data processing. It contains ready functions for matrix manipulations and image visualization and allows a program to have modular structure. Because of these facts MATLAB has been chosen as prototyping software.

2.1 System design

Before any m-files have been written, the system designhas been done. A system design for the HSV (hue, saturation and value) color system based retrieval process is presented in Figure 1. Similar design has been done for all used color systems.

Figure 1: Function chart for HSV color space with 27 bins histogram.

Tesths27 is the main function for this color system and this number of bins. It calls other functions(hs27read, dif_hsv and image_pos) when needed. Each

color system has a main function of its own and variable number (2 – 3) of sub-functions. If there is no need for color space conversion there are 2 functions,otherwise 3 functions on the first branch of the function chart.

The function call of the main function is: matches=tesths27(imagen,directory,num)

The variable imagen specifies the query image’s name and path. The directory is a path of the image database and num is a desired number of retrieved images.

2.2 Functions

At this moment there are functions implemented for four color spaces: HSV, L*a*b*, RGB and XYZ [7]. Each color space has from 2 to 4 implementations for different numbers of bins. There are altogether 14 main functions.

For some color systems it is possible to make these functions dynamic, i.e. dynamic histogram calculation. Every color system / bin combination requires its own histograms and these can be made only with an exhaustive method (pixel by pixel). Histogram calculation takes ? - 5 minutes per image, each

approximately 320×240 pixels, depending on the complexity of the color space on 150 MHz Pentium. Thus it is not reasonable to let the user select a bin number freely, especially in the case of large databases.

The functions have been named so that the names contain information of the color space used, the purpose of the functions and the number of used bins. Some functions, for example image_pos, have been used by many or all main functions and these functions have not been named as described above.

The main function checks, if the function call is correct. If the query image’s name doesn’t contain a path, the function assumes that the image is situated in the database directory. In addition

to this, the main function checks, if the query image already has a histogram in the currently used database. If the required histogram is not there, the image read (for example hs27read) function is called. This function also normalizes pixel values and arranges image matrix data to a vector format. After that stage a color space conversion function (if needed) is called. Finally a quantization function builds the histogram with the correct number of bins.

The histogram will then be saved into the database directory. If the histogram already exists there, the three previous steps will not be executed. Now the query image has been analyzed. Then the main function will go through all images in the database directory with an almost similar algorithm as in the case of the query image. The difference is that now there will be a histogram difference calculation between the query image’s and current image’s histogram. Finally the image_pos function will be used to put a query image and the desired number of best match images on the display.

2.3 Linking

It is not possible to use a program before the main function and sub-functions are connected to each other. The main function will be called from the command line or through the graphical user interface, which will be presented later in this paper. In both cases the function call will contain the same arguments. For multi-level search purposes separate main functions have been implemented, but it is possible to utilize “normal” functions and add one parameter, where the best matches array can be transferred for second a stage comparison function.

The main function calls an image read function with the image’s name. The histogram will be returned to the main function. If a color space conversion is needed, the conversion function will be called from the read function with r, g and b –vectors. The histogram will be returned to the calling function. Finally the histogram build function will be called with converted color vectors. This function returns a quantized histogram, which will go through all functions until it achieves the main function.

The main function calls the histogram difference function with two histogram vectors and will get a difference value as a response. The difference function uses Euclidean-distance calculation, but it can be easily changed to

another algorithm due to the modularity of the program. If the difference is smaller than largest difference on a best match table, the current result will be written over the last result on the best match table. After that the table is arranged again in an ascending order of distance. When all the images have been analyzed, the sorted best match table, the number of desired output images, the query image’s name, the search image’s path and the database

path are transferred to the image_pos function. These values can be transferred into larger components (vectors/containers). Now the program works faster with several input arguments, because there is no need for

picking up variables from a container.

2.4 Graphical user interface

The graphical user interface (GUI) is an important part of software development. The

designing of the GUI have to solve the following problems: learning time, speed of performance, rate of errors by users, retention over time, and subjective satisfaction [9]. This software is, at the moment, intended to be used only for testing purposes. The most important property of this software is that the results of different test queries can be seen quickly and the

results can be saved safely on a disk. Thus the visual layout is not as important as in case of a commercial software product.

In Figure 2 the first screen on GUI is presented. The purposes of the buttons, menus and other components will be presented later. If this software is developed into a commercial product, the menu bar will be disabled in the future and the exit and help buttons will be added on the canvas.

Figure 2: GUI before the search image selection.

In Figure 3 the search screen is presented just before starting a search. The user is shown a search image,and in this way he/she can be sure that the search will be made with the correct image.

Figure 3: GUI just before running a query.

The results of the query will be presented on the screen in the format which is presented in Figure 6.

3. Using the software

The first screen has already been presented in Figure 2. The user can choose from pop-up menus (see Figure 4), if the search is made with one a color system or as a multi-level search. In a one-level search a roughly quantized or a more accurate histogram is used in one loop (one color system).

Figure 4: Color system selection from a popup menu.

The second menu is disabled because a one-level search is selected.

In a multi-level search two different color systems /histograms are used. During the first loop the roughly quantized histograms are used and during the second loop.

the more accurate histograms are utilized for the best matches from the first loop. The color system on the second loop can be either the same as on the first loop or a different one. For queries with one-level search the selection of a second color system is disabled. The user can select the number of retrieved images at the final stage. The software can be linked to many image databases and the user can select a database where the query will be directed.

The user can select a search image either from the same database where the query will be directed to (default) or from any directory in his/her PC. The selection will be made with the file –open dialog, which is presented in

Figure 5. The form can be cleared with the “Reset” button. A query is executed with the “Search” button. Finally the results of the search will appear on the screen in a separate window, as presented in Figure 6. Earlier [8] the software

opened each image in a separate window and evaluating/saving the results is more difficult than after the improvement. In the top left top corner is the original query image. Below that image the best matches are presented in a descending order of similarity from left to right and from top to bottom. The user can select suitable images for further use with the “Copy selected” or the “Print selected” buttons. The “New search” button closes this form and go es back to the original search form. The “Search similar” button executes a new search where a query histogram is composed of histograms of the selected

images. If the user has selected a larger number than 21 as “Number of matching images”, the best match es will be shown on multiple screens. The user can browse these pages with the “Previous page” and “Following page”

buttons.

Figure 5: The query image selection dialog. The language of the dialog depends on the language of the operating system used.

Figure 6: The results of a query will be presented graphically.

4. Summary

The color content-based retrieval requires algorithms, which give visually correct results. Correctly working algorithms can not be chosen before simulations. The software presented in this

paper is intended to be used

for testing purposes. Some operations will be implemented, if the software is developed into a commercial product. Some modifications are under

construction.

This software has been used as a testing platform for histogram quantization tests. The modularity of this program makes it possible to take new algorithms as a part of the software in a short time. MATLAB makes

quick prototyping possible. A possibility to save figures (search results) directly on a disk is a

ful fillment of the program’s requirements. After the results have been analyzed visually, the best algorithms will be taken as a part of the final software.

5. Acknowledgements

This work has been founded by the European Union– ERDF, the Technology Development Centre Tekes, Alma Media, the Helsinki Telephone Company, Nokia Research Center, the Satakunta High Technology

Foundation and Ulla Tuominen’s Foundation

中文译文

原型基于颜色的图像检索与MATLAB

·摘要

基于内容的检索数据库(图像)已经变得越来越受欢迎。为了达到这一目的，需要发展算法检测/模拟工具，但市场上没有合适的商业工具。

本文介绍了一个模拟环境，能够从数据库中检索图像直方图的相似之处。该环境允许使用不同的色彩空间及柱，通过MA TLAB实现算法。每一种颜色体系都有自己的m-files文件。

这个阶段的软件建设过程是从系统出发设计的pre-sented图形用户界面(GUI)，对GUI 作了简短的功能描述。

1.介绍

现阶段图像数据库有成千上万的数字图像，如果用户想要找一个适合他/她目的的图象，他/她必须寻遍整个数据库直到检索出正确的图片，或使用相关资料或者一些智能软件。为了最终用户，视频点播服务也需要一个智能搜索系统。视频点播系统的检索方式与图像数据库检索方式略有不同。

如果图像采用一个有效的方法排列，一些相关资料是一个不错的选择，例如：1）范畴：动物、旗帜等等；2）名字（需要一个好的命名法）；3)日期：一位有经验的用户可以如同文本检索(关键词必须插入一个数据库)一样，有效地运用这些系统。还有使用多语言系统的情况。一种基于图象特性的工具可以是语言独立的，一种独立语言系统可以利用最佳属性进行搜索，这些属性可以有颜色、形态、质感、空间定位的形状等。

在MuVi-project中，这种工具正在建设中，它涵盖了以上各种性质。对基于内容的图像检索的研究工作,已经在文献[2 - 6]中完成了。该系统，文中已经提到过，是一种模拟环境，在它上面，基于颜色内容检索的MuVi已经开发并测试了。

2. 系统开发

MATLAB是一种有效的矢量和矩阵的数据处理程序。它包含了完整的矩阵运算函数和图像可视化功能且允许程序有模块化结构。因为以上这些因素，MATLAB被选为原型软件。

2.1系统设计

要在撰写m-files之前,完成这个系统设计。HSV(色调、饱和度、亮度)系统设计，颜色系统检索过程如图1。类似的设计已为所有颜色系统使用。

图1：HSV颜色空间及27个柱的流程图。

Tesths27是这个颜色系统及这些柱的最主要功能。它可以根据需要调用其他功能

(hs27read,dif_hsv和image_pos)。每个颜色系统都有自己的主要功能和可变数目(2-3)的子功能。如果需要颜色空间转换，在功能表的第一个分支上有两个或者三个此功能。

主函数的调用功能：

matches=tesths27(imagen,directory,num)

imagen指定变量名称及查询图像的路径。

Directory是图像数据库的路径,num是图像检索的预期数字。

2.2功能

到目前为止已实现了四个色彩空间:HSV、L*a*b*、RGB和XYZ。每一种颜色空间已从2到4实现了不同数量的柱。共计14个主要功能。

对于某些颜色系统使得这些动态功能成为可能，即动态直方图计算。每个颜色系统/柱组需要依靠自身直方图，这些可以仅用一个详尽的方法(像素x像素)实现。直方图计算每张图片要花费?- 5分钟，每张大约320×240个像素，根据每150MHz 奔腾上，颜色空间的复杂性。因此让每个用户自由选择每个柱的编号是不切实际的，特别是在大型数据库。

这些函数之所以如此命名，是因为这些名称当中包含了色彩空间的选择，函数功能柱的数目。一些函数,比如image_pos已经应用于许多或许所有的主函数当中，并且这些函数并未按照上述方法命名。

主函数的功能测试，如果这个函数调用是正确的。如果这个查询图像的名字不包含路径，该函数就会默认图像位于数据库目录下。除此之外，最主要的功能测试，查询图像的直方图是否已经位于当前数据库中。如果被查询的直方图不在数据库中，则调用图像读取(例如

hs27read)函数。该函数还可以使像素值规格化，以及将图像矩阵数据矢量化。该阶段之后，将调用一个颜色空间转换函数(如有需要)。最后调用一个量化函数根据正确数目的柱建立直方图。

该直方图将被保存到数据库目录下。如果直方图已经存在，之前的三步骤将不会被执行。现在对这个查询图像的已经完成分析。然后主函数将依照查询图像，采用相似性算法，将图像数据库目录下的所有图像检索一遍，不同的是,现在将会在当前的检索图像直方图与查询

中英文文献翻译

毕业设计（论文）外文参考文献及译文 英文题目Component-based Safety Computer of Railway Signal Interlocking System 中文题目模块化安全铁路信号计算机联锁系统 学院自动化与电气工程学院 专业自动控制 姓名葛彦宁 学号 200808746 指导教师贺清 2012年5月30日

Component-based Safety Computer of Railway Signal Interlocking System 1 Introduction Signal Interlocking System is the critical equipment which can guarantee traffic safety and enhance operational efficiency in railway transportation. For a long time, the core control computer adopts in interlocking system is the special customized high-grade safety computer, for example, the SIMIS of Siemens, the EI32 of Nippon Signal, and so on. Along with the rapid development of electronic technology, the customized safety computer is facing severe challenges, for instance, the high development costs, poor usability, weak expansibility and slow technology update. To overcome the flaws of the high-grade special customized computer, the U.S. Department of Defense has put forward the concept：we should adopt commercial standards to replace military norms and standards for meeting consumers’demand [1]. In the meantime, there are several explorations and practices about adopting open system architecture in avionics. The United Stated and Europe have do much research about utilizing cost-effective fault-tolerant computer to replace the dedicated computer in aerospace and other safety-critical fields. In recent years, it is gradually becoming a new trend that the utilization of standardized components in aerospace, industry, transportation and other safety-critical fields. 2 Railways signal interlocking system 2.1 Functions of signal interlocking system The basic function of signal interlocking system is to protect train safety by controlling signal equipments, such as switch points, signals and track units in a station, and it handles routes via a certain interlocking regulation. Since the birth of the railway transportation, signal interlocking system has gone through manual signal, mechanical signal, relay-based interlocking, and the modern computer-based Interlocking System. 2.2 Architecture of signal interlocking system Generally, the Interlocking System has a hierarchical structure. According to the function of equipments, the system can be divided to the function of equipments; the system

文献翻译英文原文

https://www.sodocs.net/doc/d813526974.html,/finance/company/consumer.html Consumer finance company The consumer finance division of the SG group of France has become highly active within India. They plan to offer finance for vehicles and two-wheelers to consumers, aiming to provide close to Rs. 400 billion in India in the next few years of its operations. The SG group is also dealing in stock broking, asset management, investment banking, private banking, information technology and business processing. SG group has ventured into the rapidly growing consumer credit market in India, and have plans to construct a headquarters at Kolkata. The AIG Group has been approved by the RBI to set up a non-banking finance company (NBFC). AIG seeks to introduce its consumer finance and asset management businesses in India. AIG Capital India plans to emphasize credit cards, mortgage financing, consumer durable financing and personal loans. Leading Indian and international concerns like the HSBC, Deutsche Bank, Goldman Sachs, Barclays and HDFC Bank are also waiting to be approved by the Reserve Bank of India to initiate similar operations. AIG is presently involved in insurance and financial services in more than one hundred countries. The affiliates of the AIG Group also provide retirement and asset management services all over the world. Many international companies have been looking at NBFC business because of the growing consumer finance market. Unlike foreign banks, there are no strictures on branch openings for the NBFCs. GE Consumer Finance is a section of General Electric. It is responsible for looking after the retail finance operations. GE Consumer Finance also governs the GE Capital Asia. Outside the United States, GE Consumer Finance performs its operations under the GE Money brand. GE Consumer Finance currently offers financial services in more than fifty countries. The company deals in credit cards, personal finance, mortgages and automobile solutions. It has a client base of more than 118 million customers throughout the world

关于一些MATLAB用法和器件名称的中英文翻译

MATLAB - The Language Of Technical Computing MATLAB is a high-level language and interactive environment that enables you to perform computationally intensive tasks faster than with traditional programming languages such as C, C++, and Fortran. You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas. MATLAB provides a number of features for documenting and sharing your work. You can integrate your MATLAB code with other languages and applications, and distribute your MATLAB algorithms and applications. MATLAB has key featuers as follows:（1）High-level language for technical computing （2）Development environment for managing code, files, and data（3）Interactive tools for iterative exploration, design, and problem solving 4）Mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, and numerical integration （5）2-D and 3-D graphics functions for visualizing data （6）Tools for building custom graphical user interfaces （7）Functions for integrating MATLAB based algorithms with external applications and languages, such as C, C++, Fortran, Java, COM, and Microsoft Excel The MATLAB language supports the vector and matrix operations that are fundamental to engineering and scientific problems. It enables fast development and execution. With the MATLAB language, you can program and develop algorithms faster than with traditional languages because you do not need to perform low-level administrative tasks, such as declaring variables, specifying data types, and allocating memory. In many cases, MATLAB eliminates the nee d for ‘for’ loops. As a result, one line of MATLAB code can often replace several lines of C or C++ code. At the same time, MATLAB provides all the features of a traditional programming language, including arithmetic operators, flow control, data structures, data types, object-oriented programming (OOP), and debugging features. MATLAB lets you execute commands or groups of commands one at a time, without compiling and linking, enabling you to quickly iterate to the optimal solution. For fast execution of heavy matrix and vector computations, MATLAB uses processor-optimized libraries. For general-purpose scalar computations, MATLAB generates machine-code instructions using its JIT (Just-In-Time) compilation technology. This technology, which is available on most platforms, provides execution speeds that rival those of traditional programming languages. MATLAB includes development tools that help you implement your algorithm efficiently. These include the following: MATLAB Editor - Provides standard editing and debugging features, such as setting breakpoints and single stepping. M-Lint Code Checker - Analyzes your code and recommends changes to improve its performance and maintainability. MATLAB Profiler - Records the time spent executing each line of code. Directory Reports- Scan all the files in a directory and report on code efficiency, file differences, file dependencies, and code coverage。 You can use the interactive tool GUIDE (Graphical User Interface Development Environment) to lay out, design, and edit user interfaces. GUIDE lets you include list

计算机专业外文文献及翻译

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