Parallel Image Processing on Configurable Computing Architecture
图像处理中值滤波器中英文对照外文翻译文献
中英文资料对照外文翻译一、英文原文A NEW CONTENT BASED MEDIAN FILTERABSTRACTIn this paper the hardware implementation of a contentbased median filter suitabl e for real-time impulse noise suppression is presented. The function of the proposed ci rcuitry is adaptive; it detects the existence of impulse noise in an image neighborhood and applies the median filter operator only when necessary. In this way, the blurring o f the imagein process is avoided and the integrity of edge and detail information is pre served. The proposed digital hardware structure is capable of processing gray-scale im ages of 8-bit resolution and is fully pipelined, whereas parallel processing is used to m inimize computational time. The architecturepresented was implemented in FPGA an d it can be used in industrial imaging applications, where fast processing is of the utm ost importance. The typical system clock frequency is 55 MHz.1. INTRODUCTIONTwo applications of great importance in the area of image processing are noise filtering and image enhancement [1].These tasks are an essential part of any image pro cessor,whether the final image is utilized for visual interpretation or for automatic an alysis. The aim of noise filtering is to eliminate noise and its effects on the original im age, while corrupting the image as little as possible. To this end, nonlinear techniques (like the median and, in general, order statistics filters) have been found to provide mo re satisfactory results in comparison to linear methods. Impulse noise exists in many p ractical applications and can be generated by various sources, including a number of man made phenomena, such as unprotected switches, industrial machines and car ign ition systems. Images are often corrupted by impulse noise due to a noisy sensor or ch annel transmission errors. The most common method used for impulse noise suppressi on n forgray-scale and color images is the median filter (MF) [2].The basic drawback o f the application of the MF is the blurringof the image in process. In the general case,t he filter is applied uniformly across an image, modifying pixels that arenot contamina ted by noise. In this way, the effective elimination of impulse noise is often at the exp ense of an overalldegradation of the image and blurred or distorted features[3].In this paper an intelligent hardware structure of a content based median filter (CBMF) suita ble for impulse noise suppression is presented. The function of the proposed circuit is to detect the existence of noise in the image window and apply the corresponding MFonly when necessary. The noise detection procedure is based on the content of the im age and computes the differences between the central pixel and thesurrounding pixels of a neighborhood. The main advantage of this adaptive approach is that image blurrin g is avoided and the integrity of edge and detail information are preserved[4,5]. The pro posed digital hardware structure is capable of processing gray-scale images of 8-bitres olution and performs both positive and negative impulse noise removal. The architectt ure chosen is based on a sequence of four basic functional pipelined stages, and parall el processing is used within each stage. A moving window of a 3×3 and 5×5-pixel im age neighborhood can be selected. However, the system can be easily expanded to acc ommodate windows of larger sizes. The proposed structure was implemented using fi eld programmable gate arrays (FPGA). The digital circuit was designed, compiled and successfully simulated using the MAX+PLUS II Programmable Logic Development S ystem by Altera Corporation. The EPF10K200SFC484-1 FPGA device of the FLEX1 0KE device family was utilized for the realization of the system. The typical clock fre quency is 55 MHz and the system can be used for real-time imaging applications whe re fast processing is required [6]. As an example,the time required to perform filtering of a gray-scale image of 260×244 pixels is approximately 10.6 msec.2. ADAPTIVE FILTERING PROCEDUREThe output of a median filter at a point x of an image f depends on the values of t he image points in the neighborhood of x. This neighborhood is determined by a wind ow W that is located at point x of f including n points x1, x2, …, xn of f, with n=2k+1. The proposed adaptive content based median filter can be utilized for impulse noisesu p pression in gray-scale images. A block diagram of the adaptive filtering procedure is depicted in Fig. 1. The noise detection procedure for both positive and negative noise is as follows:(i) We consider a neighborhood window W that is located at point x of the image f. Th e differences between the central pixel at point x and the pixel values of the n-1surr ounding points of the neighborhood (excluding thevalue of the central pixel) are co mputed.(ii) The sum of the absolute values of these differences is computed, denoted as fabs(x ). This value provides ameasure of closeness between the central pixel and its su rrounding pixels.(iii) The value fabs(x) is compared to fthreshold(x), which is anappropriately selected positive integer threshold value and can be modified. The central pixel is conside red to be noise when the value fabs(x) is greater than thethreshold value fthresho d(x).(iv) When the central pixel is considered to be noise it is substituted by the median val ue of the image neighborhood,denoted as fk+1, which is the normal operationof the median filter. In the opposite case, the value of the central pixel is not altered and the procedure is repeated for the next neighborhood window.From the noised etection scheme described, it should be mentioned that the noise detection level procedure can be controlled and a range of pixel values (and not only the fixedvalues of 0 and 255, salt and pepper noise) is considered asimpulse noise.In Fig. 2 the results of the application of the median filter and the CBMF in the gray-sca le image “Peppers” are depicted.More specifically, in Fig. 2(a) the original,uncor rupted image“Peppers” is depicted. In Fig. 2(b) the original imagedegraded by 5% both positive and negative impulse noise isillustrated. In Figs 2(c) and 2(d) the resultant images of the application of median filter and CBMF for a 3×3-pixel win dow are shown, respectively. Finally, the resultant images of the application of m edian filter and CBMF for a 5×5-pixelwindow are presented in Figs 2(e) and 2(f). It can be noticed that the application of the CBMF preserves much better edges a nddetails of the images, in comparison to the median filter.A number of different objective measures can be utilized forthe evaluation of these results. The most wi dely used measures are the Mean Square Error (MSE) and the Normalized Mean Square Error (NMSE) [1]. The results of the estimation of these measures for the two filters are depicted in Table I.For the estimation of these measures, the result ant images of the filters are compared to the original, uncorrupted image.From T able I it can be noticed that the MSE and NMSE estimatedfor the application of t he CBMF are considerably smaller than those estimated for the median filter, in all the cases.Table I. Similarity measures.3. HARDWARE ARCHITECTUREThe structure of the adaptive filter comprises four basic functional units, the mo ving window unit , the median computation unit , the arithmetic operations unit , and th e output selection unit . The input data of the system are the gray-scale values of the pi xels of the image neighborhood and the noise threshold value. For the computation of the filter output a3×3 or 5×5-pixel image neighborhood can be selected. Image input d ata is serially imported into the first stage. In this way,the total number of the inputpin s are 24 (21 inputs for the input data and 3 inputs for the clock and the control signalsr equired). The output data of the system are the resultant gray-scale values computed f or the operation selected (8pins).The moving window unit is the internal memory of the system,used for storing th e input values of the pixels and for realizing the moving window operation. The pixel values of the input image, denoted as “IMAGE_INPUT[7..0]”, areimported into this u nit in serial. For the representation of thethreshold value used for the detection of a no Filter Impulse noise 5% mse Nmse(×10-2) 3×3 5×5 3×3 5×5Median CBMF 57.554 35.287 130.496 84.788 0.317 0.194 0.718 0.467ise pixel 13 bits are required. For the moving window operation a 3×3 (5×5)-pixel sep entine type memory is used, consisting of 9 (25)registers. In this way,when the windoP1 P2 P3w is moved into the next image neighborhood only 3 or 5 pixel values stored in the memory are altered. The “en5×5” control signal is used for the selection of the size of th e image window, when“en5×5” is equal to “0” (“1”) a 3×3 (5×5)-pixel neighborhood is selected. It should be mentioned that the modules of the circuit used for the 3×3-pix el window are utilized for the 5×5-pixel window as well. For these modules, 2-to-1mu ltiplexers are utilized to select the appropriate pixel values,where necessary. The mod ules that are utilized only in the case of the 5×5-pixel neighborhood are enabled by th e“en5×5” control signal. The outputs of this unit are rows ofpixel values (3 or 5, respe ctively), which are the inputs to the median computation unit.The task of the median c omputation unit is to compute themedian value of the image neighborhood in order to substitutethe central pixel value, if necessary. For this purpose a25-input sorter is utili zeed. The structure of the sorter has been proposed by Batcher and is based on the use of CS blocks. ACS block is a max/min module; its first output is the maximumof the i nputs and its second output the minimum. The implementation of a CS block includes a comparator and two 2-to-1 multiplexers. The outputs values of the sorter, denoted a s “OUT_0[7..0]”…. “OUT_24[7..0]”, produce a “sorted list” of the 25 initial pixel val ues. A 2-to-1 multiplexer isused for the selection of the median value for a 3×3 or 5×5-pixel neighborhood.The function of the arithmetic operations unit is to computethe value fabs(x), whi ch is compared to the noise threshold value in the final stage of the adaptive filter.The in puts of this unit are the surrounding pixel values and the central pixelof the neighb orhood. For the implementation of the mathematical expression of fabs(x), the circuit of this unit contains a number of adder modules. Note that registers have been used to achieve a pipelined operation. An additional 2-to-1 multiplexer is utilized for the selec tion of the appropriate output value, depending on the “en5×5” control signal. From th e implementation point of view, the use of arithmetic blocks makes this stage hardwar e demanding.The output selection unit is used for the selection of the appropriateoutput value of the performed noise suppression operation. For this selection, the corresponding no ise threshold value calculated for the image neighborhood,“NOISE_THRES HOLD[1 2..0]”,is employed. This value is compared to fabs(x) and the result of the comparison Classifies the central pixel either as impulse noise or not. If thevalue fabs(x) is greater than the threshold value fthreshold(x) the central pixel is positive or negative impulse noise and has to be eliminated. For this reason, the output of the comparison is used as the selection signal of a 2-to-1 multiplexer whose inputs are the central pixel and the c orresponding median value for the image neighborhood. The output of the multiplexer is the output of this stage and the final output of the circuit of the adaptive filter.The st ructure of the CBMF, the computation procedure and the design of the four aforeme n tioned units are illustrated in Fig. 3.ImagewindoeFigure 1: Block diagram of the filtering methodFigure 2: Results of the application of the CBMF: (a) Original image, (b) noise corrupted image (c) Restored image by a 3x3 MF, (d) Restored image by a 3x3 CBMF, (e) Restored image by a 5x5 MF and (f) Restored image by a 5x5 CBMF.4. IMPLEMENTATION ISSUESThe proposed structure was implemented in FPGA,which offer an attractive com bination of low cost, high performance and apparent flexibility, using the software pa ckage+PLUS II of Altera Corporation. The FPGA used is the EPF10K200SFC484-1 d evice of the FLEX10KE device family,a device family suitable for designs that requir e high densities and high I/O count. The 99% of the logic cells(9965/9984 logic cells) of the device was utilized to implement the circuit . The typical operating clock frequ ency of the system is 55 MHz. As a comparison, the time required to perform filtering of a gray-scale image of 260×244 pixelsusing Matlab® software on a Pentium 4/2.4 G Hz computer system is approximately 7.2 sec, whereas the corresponding time using h ardware is approximately 10.6 msec.The modification of the system to accommodate windows oflarger sizes can be done in a straightforward way, requiring onlya small nu mber of changes. More specifically, in the first unit the size of the serpentine memory P4P5P6P7P8P9SubtractorarryMedianfilteradder comparatormuitiplexerf abc(x)valueand the corresponding number of multiplexers increase following a square law. In the second unit, the sorter module should be modified,and in the third unit the number of the adder devicesincreases following a square law. In the last unit no changes are requ ired.5. CONCLUSIONSThis paper presents a new hardware structure of a content based median filter, ca pable of performing adaptive impulse noise removal for gray-scale images. The noise detection procedure takes into account the differences between the central pixel and th e surrounding pixels of a neighborhood.The proposed digital circuit is capable ofproce ssing grayscale images of 8-bit resolution, with 3×3 or 5×5-pixel neighborhoods as op tions for the computation of the filter output. However, the design of the circuit is dire ctly expandableto accommodate larger size image windows. The adaptive filter was d eigned and implemented in FPGA. The typical clock frequency is 55 MHz and the sys tem is suitable forreal-time imaging applications.REFERENCES[1] W. K. Pratt, Digital Image Processing. New York: Wiley,1991.[2] G. R. Arce, N. C. Gallagher and T. Nodes, “Median filters:Theory and applicat ions,” in Advances in ComputerVision and Image Processing, Greenwich, CT: JAI, 1986.[3] T. A. Nodes and N. C. Gallagher, Jr., “The output distributionof median type filte rs,” IEEE Transactions onCommunications, vol. COM-32, pp. 532-541, May1984.[4] T. Sun and Y. Neuvo, “Detail-preserving median basedfilters in imageprocessing,” Pattern Recognition Letters,vol. 15, pp. 341-347, Apr. 1994.[5] E. Abreau, M. Lightstone, S. K. Mitra, and K. Arakawa,“A new efficient approachfor the removal of impulsenoise from highly corrupted images,” IEEE Transa ctionson Image Processing, vol. 5, pp. 1012-1025, June 1996.[6] E. R. Dougherty and P. Laplante, Introduction to Real-Time Imaging, Bellingham:SPIE/IEEE Press, 1995.二、英文翻译基于中值滤波的新的内容摘要在本设计中的提出了基于中值滤波的硬件实现用来抑制脉冲噪声的干扰。
常用的图形库
ESRI
PCI Geomatics
Digital Grove
JDMCox Global Mapper Software(USAPhotoMaps) 3DEM
MARPLOT
Map Maker
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geoTIFF Examiner
shapechk
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Graphics:Non-Photo-Realistic Rendering(NPR)
NPR
Shadows for Cel Animation
David Salesin
Virtual Clay: A Deformation NPRQuake Model by 3D Cellular Automata
GRAIL: Graphics and Imaging Laboratory
Microsoft Research: UCSC SciVis Graphics
Visualization Lab SUNY at Stony VRVis Brook, New York
Institut of Computer Graphics and Algorithms
tiff library
Independent JPEG Group
FFTW
paintlib
png library
libungif
Gnuplot
Interactive Data ImageMagick Language(IDL) Matlab
IPL98
CVIPtools
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Open Source Computer VXL Vision Library JBIG-KIT
System-level
This research was partly sponsored by the JESSI AC75 project of the EC
1
Abstract
Application studies in the areas of image and video processing systems indicate that between 50 and 80% of the area cost in (application-speci c) architectures for realtime multi-dimensional signal processing (RMSP) is due to memory units , i.e. single or multi-port RAMs, pointer-addressed memories, and register les. This is true for both single-processor and weakly parallel processor realizations. This paper has two main contributions. First, to reduce this dominant cost, we propose to address the systemlevel storage organization for the multi-dimensional (M-D) signals as a rst step in the overall methodology to map these applications, even before the parallelization and load balancing. Secondly, we will demonstrate the usefulness of this novel approach based on two realistic test-vehicles, namely a cavity detector for medical image processing and a quad-tree based image coding application. The novel design results for these relevant applications are useful as such.
CCF推荐的国际学术会议和期刊目录修订版发布
CCF推荐的国际学术会议和期刊目录修订版发布CCF(China Computer Federation中国计算机学会)于2010年8月发布了第一版推荐的国际学术会议和期刊目录，一年来，经过业内专家的反馈和修订，于日前推出了修订版，现将修订版予以发布。
本次修订对上一版内容进行了充实，一些会议和期刊的分类排行进行了调整，目录包括：计算机科学理论、计算机体系结构与高性能计算、计算机图形学与多媒体、计算机网络、交叉学科、人工智能与模式识别、软件工程/系统软件/程序设计语言、数据库/数据挖掘/内容检索、网络与信息安全、综合刊物等方向的国际学术会议及期刊目录，供国内高校和科研单位作为学术评价的参考依据。
目录中，刊物和会议分为A、B、C三档。
A类表示国际上极少数的顶级刊物和会议，鼓励我国学者去突破；B类是指国际上著名和非常重要的会议、刊物，代表该领域的较高水平，鼓励国内同行投稿；C类指国际上重要、为国际学术界所认可的会议和刊物。
这些分类目录每年将学术界的反馈和意见，进行修订，并逐步增加研究方向。
中国计算机学会推荐国际学术刊物（网络/信息安全）一、 A类序号刊物简称刊物全称出版社网址1. TIFS IEEE Transactions on Information Forensics andSecurity IEEE /organizations/society/sp/tifs.html2. TDSC IEEE Transactions on Dependable and Secure ComputingIEEE /tdsc/3. TISSEC ACM Transactions on Information and SystemSecurity ACM /二、 B类序号刊物简称刊物全称出版社网址1. Journal of Cryptology Springer /jofc/jofc.html2. Journal of Computer SecurityIOS Press /jcs/3. IEEE Security & Privacy IEEE/security/4. Computers &Security Elsevier http://www.elsevier.nl/inca/publications/store/4/0/5/8/7/7/5. JISecJournal of Internet Security NahumGoldmann. /JiSec/index.asp6. Designs, Codes andCryptography Springer /east/home/math/numbers?SGWID=5 -10048-70-35730330-07. IET Information Security IET /IET-IFS8. EURASIP Journal on InformationSecurity Hindawi /journals/is三、C类序号刊物简称刊物全称出版社网址1. CISDA Computational Intelligence for Security and DefenseApplications IEEE /2. CLSR Computer Law and SecurityReports Elsevier /science/journal/026736493. Information Management & Computer Security MCB UniversityPress /info/journals/imcs/imcs.jsp4. Information Security TechnicalReport Elsevier /locate/istr中国计算机学会推荐国际学术会议（网络/信息安全方向）一、A类序号会议简称会议全称出版社网址1. S&PIEEE Symposium on Security and Privacy IEEE /TC/SP-Index.html2. CCSACM Conference on Computer and Communications Security ACM /sigs/sigsac/ccs/3. CRYPTO International Cryptology Conference Springer-Verlag /conferences/二、B类序号会议简称会议全称出版社网址1. SecurityUSENIX Security Symposium USENIX /events/2. NDSSISOC Network and Distributed System Security Symposium Internet Society /isoc/conferences/ndss/3. EurocryptAnnual International Conference on the Theory and Applications of Cryptographic Techniques Springer /conferences/eurocrypt2009/4. IH Workshop on Information Hiding Springer-Verlag /~rja14/ihws.html5. ESORICSEuropean Symposium on Research in Computer Security Springer-Verlag as.fr/%7Eesorics/6. RAIDInternational Symposium on Recent Advances in Intrusion Detection Springer-Verlag /7. ACSACAnnual Computer Security Applications ConferenceIEEE /8. DSNThe International Conference on Dependable Systems and Networks IEEE/IFIP /9. CSFWIEEE Computer Security Foundations Workshop /CSFWweb/10. TCC Theory of Cryptography Conference Springer-Verlag /~tcc08/11. ASIACRYPT Annual International Conference on the Theory and Application of Cryptology and Information Security Springer-Verlag /conferences/ 12. PKC International Workshop on Practice and Theory in Public Key Cryptography Springer-Verlag /workshops/pkc2008/三、 C类序号会议简称会议全称出版社网址1. SecureCommInternational Conference on Security and Privacy in Communication Networks ACM /2. ASIACCSACM Symposium on Information, Computer and Communications Security ACM .tw/asiaccs/3. ACNSApplied Cryptography and Network Security Springer-Verlag /acns_home/4. NSPWNew Security Paradigms Workshop ACM /current/5. FC Financial Cryptography Springer-Verlag http://fc08.ifca.ai/6. SACACM Symposium on Applied Computing ACM /conferences/sac/ 7. ICICS International Conference on Information and Communications Security Springer /ICICS06/8. ISC Information Security Conference Springer /9. ICISCInternational Conference on Information Security and Cryptology Springer /10. FSE Fast Software Encryption Springer http://fse2008.epfl.ch/11. WiSe ACM Workshop on Wireless Security ACM /~adrian/wise2004/12. SASN ACM Workshop on Security of Ad-Hoc and Sensor Networks ACM /~szhu/SASN2006/13. WORM ACM Workshop on Rapid Malcode ACM /~farnam/worm2006.html14. DRM ACM Workshop on Digital Rights Management ACM /~drm2007/15. SEC IFIP International Information Security Conference Springer http://sec2008.dti.unimi.it/16. IWIAIEEE International Information Assurance Workshop IEEE /17. IAWIEEE SMC Information Assurance Workshop IEEE /workshop18. SACMATACM Symposium on Access Control Models and Technologies ACM /19. CHESWorkshop on Cryptographic Hardware and Embedded Systems Springer /20. CT-RSA RSA Conference, Cryptographers' Track Springer /21. DIMVA SIG SIDAR Conference on Detection of Intrusions and Malware and Vulnerability Assessment IEEE /dimva200622. SRUTI Steps to Reducing Unwanted Traffic on the Internet USENIX /events/23. HotSecUSENIX Workshop on Hot Topics in Security USENIX /events/ 24. HotBots USENIX Workshop on Hot Topics in Understanding Botnets USENIX /event/hotbots07/tech/25. ACM MM&SEC ACM Multimedia and Security Workshop ACM。
红楼梦论文外文参考文献大全
红楼梦论文外文参考文献大全《红楼梦》复杂的版本问题历来为学界所关注。
20世纪20年代以来，陆续发现的抄本刻本就达13种之多，且彼此间存在相当数量的异文。
在何本为最好的版本这一问题上，学界的评价标准呈现多元化特征。
其中，从校勘学的研究视角，以是否贴近曹雪芹原笔原意，是否“真本”作为“优本”评判标准的观点占主流，以文字的艺术效果作为评判标准的观点占少数。
我们在这里了一些红楼梦论文外文参考文献，希望对你有用。
1、陆超逸。
AContrastiveStudyoftheTranslationofCultural-SpecificContent inHongloumeng[J].海外英语。
xx（05）2、王丽鸽。
OnEnglishTranslationofCharacters'NamesofTwoEnglishVersionso fADreamofRedMansions[J].海外英语。
xx（16）3、李淑曼。
浅析《红楼梦》第三回中异化归化译法现象[J].北方文学。
xx（18）4、余丽娟。
TheTranslationoftheFuzzyNumeralsinADreamofRedMansions[J].教师。
xx（09）5、俞心瑶。
TranslationsofUnreliableNarrativeinHongloumeng:AComparativeStudyonADreamofRedMansionsandTheStoryoftheStone[J].疯狂英语（理论版）。
xx（02）6、高林芝。
OntheEnglishTranslationofPortraitDescriptionintheTwoVersion sofADreamofRedMansionsfromthePerspectiveofDiction[J].校园英语。
xx（13）7、张曦。
OntheTranslationofIdiomsinADreamofRedMansions[J].台声。
计算机专用术语英文及中文翻译
计算机术语大全1、CPU3DNow!（3D no waiting，无须等待的3D处理）AAM（AMD Analyst Meeting，AMD分析家会议）ABP（Advanced Branch Prediction，高级分支预测）ACG（Aggressive Clock Gating，主动时钟选择）AIS（Alternate Instruction Set，交替指令集）ALA T（advanced load table，高级载入表）ALU（Arithmetic Logic Unit，算术逻辑单元）Aluminum（铝）AGU（Address Generation Units，地址产成单元）APC（Advanced Power Control，高级能源控制）APIC（Advanced rogrammable Interrupt Controller，高级可编程中断控制器）APS（Alternate Phase Shifting，交替相位跳转）ASB（Advanced System Buffering，高级系统缓冲）A TC（Advanced Transfer Cache，高级转移缓存）A TD（Assembly Technology Development，装配技术发展）BBUL（Bumpless Build-Up Layer，内建非凹凸层）BGA（Ball Grid Array，球状网阵排列）BHT（branch prediction table，分支预测表）Bops（Billion Operations Per Second，10亿操作秒）BPU（Branch Processing Unit，分支处理单元）BP（Brach Pediction，分支预测）BSP（Boot Strap Processor，启动捆绑处理器）BTAC（Branch Target Address Calculator，分支目标寻址计算器）CBGA (Ceramic Ball Grid Array，陶瓷球状网阵排列)CDIP (Ceramic Dual-In-Line，陶瓷双重直线)Center Processing Unit Utilization，中央处理器占用率CFM（cubic feet per minute，立方英尺秒）CMT（course-grained multithreading，过程消除多线程）CMOS（Complementary Metal Oxide Semiconductor，互补金属氧化物半导体）CMOV（conditional move instruction，条件移动指令）CISC（Complex Instruction Set Computing，复杂指令集计算机）CLK（Clock Cycle，时钟周期）CMP（on-chip multiprocessor，片内多重处理）CMS（Code Morphing Software，代码变形软件）co-CPU（cooperative CPU，协处理器）COB（Cache on board，板上集成缓存，做在CPU卡上的二级缓存，通常是内核的一半速度））COD（Cache on Die，芯片内核集成缓存）Copper（铜）CPGA（Ceramic Pin Grid Array，陶瓷针型栅格阵列）CPI（cycles per instruction，周期指令）CPLD（Complex Programmable Logic Device，複雜可程式化邏輯元件）CPU（Center Processing Unit，中央处理器）CRT（Cooperative Redundant Threads，协同多余线程）CSP（Chip Scale Package，芯片比例封装）CXT（Chooper eXTend，增强形K6-2内核，即K6-3）Data Forwarding（数据前送）dB（decibel，分贝）DCLK（Dot Clock，点时钟）DCT（DRAM Controller，DRAM控制器）DDT（Dynamic Deferred Transaction，动态延期处理）Decode（指令解码）DIB（Dual Independent Bus，双重独立总线）DMT（Dynamic Multithreading Architecture，动态多线程结构）DP（Dual Processor，双处理器）DSM（Dedicated Stack Manager，专门堆栈管理）DSMT（Dynamic Simultaneous Multithreading，动态同步多线程）DST（Depleted Substrate Transistor，衰竭型底层晶体管）DTV（Dual Threshold V oltage，双重极限电压）DUV（Deep Ultra-Violet，纵深紫外光）EBGA（Enhanced Ball Grid Array，增强形球状网阵排列）EBL（electron beam lithography，电子束平版印刷）EC（Embedded Controller，嵌入式控制器）EDB（Execute Disable Bit，执行禁止位）EDEC（Early Decode，早期解码）Embedded Chips（嵌入式）EM64T（Extended Memory 64 Technology，扩展内存64技术）EPA（edge pin array，边缘针脚阵列）EPF（Embedded Processor Forum，嵌入式处理器论坛）EPL（electron projection lithography，电子发射平版印刷）EPM（Enhanced Power Management，增强形能源管理）EPIC（explicitly parallel instruction code，并行指令代码）EUV（Extreme Ultra Violet，紫外光）EUV（extreme ultraviolet lithography，极端紫外平版印刷）FADD（Floationg Point Addition，浮点加）FBGA（Fine-Pitch Ball Grid Array，精细倾斜球状网阵包装）FBGA（flipchip BGA，轻型芯片BGA）FC-BGA（Flip-Chip Ball Grid Array，翻转芯片球形网阵包装）FC-LGA（Flip-Chip Land Grid Array，翻转接点网阵包装）FC-PGA（Flip-Chip Pin Grid Array，翻转芯片球状网阵包装）FDIV（Floationg Point Divide，浮点除）FEMMS：Fast EntryExit Multimedia State，快速进入退出多媒体状态FFT（fast Fourier transform，快速热欧姆转换）FGM（Fine-Grained Multithreading，高级多线程）FID（FID：Frequency identify，频率鉴别号码）FIFO（First Input First Output，先入先出队列）FISC（Fast Instruction Set Computer，快速指令集计算机）flip-chip（芯片反转）FLOPs（Floating Point Operations Per Second，浮点操作秒）FMT（fine-grained multithreading，纯消除多线程）FMUL（Floationg Point Multiplication，浮点乘）FPRs（floating-point registers，浮点寄存器）FPU（Float Point Unit，浮点运算单元）FSUB（Floationg Point Subtraction，浮点减）GFD（Gold finger Device，金手指超频设备）GHC（Global History Counter，通用历史计数器）GTL（Gunning Transceiver Logic，射电收发逻辑电路）GVPP（Generic Visual Perception Processor，常规视觉处理器）HL-PBGA表面黏著,高耐热、轻薄型塑胶球状网阵封装HTT（Hyper-Threading Technology，超级线程技术）Hz（hertz，赫兹，频率单位）IA（Intel Architecture，英特尔架构）IAA（Intel Application Accelerator，英特尔应用程序加速器）IA TM（Intel Advanced Thermal Manager，英特尔高级热量管理指令集）ICU（Instruction Control Unit，指令控制单元）ID（identify，鉴别号码）IDF（Intel Developer Forum，英特尔开发者论坛）IDMB（Intel Digital Media Boost，英特尔数字媒体推进指令集）IDPC（Intel Dynamic Power Coordination，英特尔动态能源调和指令集）IEU（Integer Execution Units，整数执行单元）IHS（Integrated Heat Spreader，完整热量扩展）ILP（Instruction Level Parallelism，指令级平行运算）IMM Intel Mobile Module, 英特尔移动模块Instructions Cache，指令缓存Instruction Coloring（指令分类）IOPs（Integer Operations Per Second，整数操作秒）IPC（Instructions Per Clock Cycle，指令时钟周期）ISA（instruction set architecture，指令集架构）ISD（inbuilt speed-throttling device，内藏速度控制设备）ITC（Instruction Trace Cache，指令追踪缓存）ITRS（International Technology Roadmap for Semiconductors，国际半导体技术发展蓝图）KNI（Katmai New Instructions，Katmai新指令集，即SSE）Latency（潜伏期）LDT（Lightning Data Transport，闪电数据传输总线）LFU（Legacy Function Unit，传统功能单元）LGA（land grid array，接点栅格阵列）LN2（Liquid Nitrogen，液氮）Local Interconnect（局域互连）MAC（multiply-accumulate，累积乘法）mBGA (Micro Ball Grid Array，微型球状网阵排列)nm（namometer，十亿分之一米毫微米）MCA（Machine Check Architecture，机器检查架构）MCU（Micro-Controller Unit，微控制器单元）MCT（Memory Controller，内存控制器）MESI（Modified, Exclusive, Shared, Invalid：修改、排除、共享、废弃）MF（MicroOps Fusion，微指令合并）mm（micron metric，微米）MMX（MultiMedia Extensions，多媒体扩展指令集）MMU（Multimedia Unit，多媒体单元）MMU（Memory Management Unit，内存管理单元）MN（model numbers，型号数字）MFLOPS（Million Floationg PointSecond，每秒百万个浮点操作）MHz（megahertz，兆赫）mil（PCB 或晶片佈局的長度單位，1 mil = 千分之一英寸）MIMD（Multi Instruction Multiple Data，多指令多数据流）MIPS（Million Instruction Per Second，百万条指令秒）MOESI（Modified, Owned, Exclusive, Shared or Invalid，修改、自有、排除、共享或无效）MOF（Micro Ops Fusion，微操作熔合）Mops（Million Operations Per Second，百万次操作秒）MP（Multi-Processing，多重处理器架构）MPF（Micro processor Forum，微处理器论坛）MPU（Microprocessor Unit，微处理器）MPS（MultiProcessor Specification，多重处理器规范）MSRs（Model-Specific Registers，特别模块寄存器）MSV（Multiprocessor Specification V ersion，多处理器规范版本）MVP（Mobile V oltage Positioning，移动电压定位）IVNAOC（no-account OverClock，无效超频）NI（Non－Intel，非英特尔）NOP（no operation，非操作指令）NRE（Non-Recurring Engineering charge，非重複性工程費用）OBGA（Organic Ball Grid Arral，有机球状网阵排列）OCPL（Off Center Parting Line，远离中心部分线队列）OLGA（Organic Land Grid Array，有机平面网阵包装）OoO（Out of Order，乱序执行）OPC（Optical Proximity Correction，光学临近修正）OPGA（Organic Pin Grid Array，有机塑料针型栅格阵列）OPN（Ordering Part Number，分类零件号码）PA T（Performance Acceleration Technology，性能加速技术）PBGA（Plastic Pin Ball Grid Array，塑胶球状网阵排列）PDIP (Plastic Dual-In-Line，塑料双重直线)PDP（Parallel Data Processing，并行数据处理）PGA（Pin-Grid Array，引脚网格阵列）,耗电大PLCC (Plastic Leaded Chip Carriers，塑料行间芯片运载)Post-RISC（加速RISC，或后RISC）PPE（Power Processor Element，Power处理器元件）PPU（Physics Processing Unit，物理处理单元）PR（Performance Rate，性能比率）PIB（Processor In a Box，盒装处理器）PM（Pseudo-Multithreading，假多线程）PPGA（Plastic Pin Grid Array，塑胶针状网阵封装）PQFP（Plastic Quad Flat Package，塑料方块平面封装）PSN（Processor Serial numbers，处理器序列号）QFP（Quad Flat Package，方块平面封装）QSPS（Quick Start Power State，快速启动能源状态）RAS（Return Address Stack，返回地址堆栈）RAW（Read after Write，写后读）REE（Rapid Execution Engine，快速执行引擎）Register Contention（抢占寄存器）Register Pressure（寄存器不足）Register Renaming（寄存器重命名）Remark（芯片频率重标识）Resource contention（资源冲突）Retirement（指令引退）RISC（Reduced Instruction Set Computing，精简指令集计算机）ROB（Re-Order Buffer，重排序缓冲区）RSE（register stack engine，寄存器堆栈引擎）RTL（Register Transfer Level，暫存器轉換層。
国际计算机会议与期刊分级列表
Computer Science Department Conference RankingsSome conferences accept multiple categories of papers. The rankingsbelow are for the most prestigious category of paper at a givenconference. All other categories should be treated as "unranked".AREA: Artificial Intelligence and Related SubjectsRank 1:IJCAI: Intl Joint Conf on AIAAAI: American Association for AI National ConferenceICAA: International Conference on Autonomous Agents（现改名为AAMAS） CVPR: IEEE Conf on Comp Vision and Pattern RecognitionICCV: Intl Conf on Computer VisionICML: Intl Conf on Machine LearningKDD: Knowledge Discovery and Data MiningKR: Intl Conf on Principles of KR & ReasoningNIPS: Neural Information Processing SystemsUAI: Conference on Uncertainty in AIACL: Annual Meeting of the ACL (Association of Computational Linguistics) Rank 2:AID: Intl Conf on AI in DesignAI-ED: World Conference on AI in EducationCAIP: Inttl Conf on Comp. Analysis of Images and PatternsCSSAC: Cognitive Science Society Annual ConferenceECCV: European Conference on Computer VisionEAI: European Conf on AIEML: European Conf on Machine LearningGP: Genetic Programming ConferenceIAAI: Innovative Applications in AIICIP: Intl Conf on Image ProcessingICNN/IJCNN: Intl (Joint) Conference on Neural NetworksICPR: Intl Conf on Pattern RecognitionICDAR: International Conference on Document Analysis and RecognitionICTAI: IEEE conference on Tools with AIAMAI: Artificial Intelligence and MathsDAS: International Workshop on Document Analysis SystemsWACV: IEEE Workshop on Apps of Computer VisionCOLING: International Conference on Computational LiguisticsEMNLP: Empirical Methods in Natural Language ProcessingRank 3:PRICAI: Pacific Rim Intl Conf on AIAAI: Australian National Conf on AIACCV: Asian Conference on Computer VisionAI*IA: Congress of the Italian Assoc for AIANNIE: Artificial Neural Networks in EngineeringANZIIS: Australian/NZ Conf on Intelligent Inf. SystemsCAIA: Conf on AI for ApplicationsCAAI: Canadian Artificial Intelligence ConferenceASADM: Chicago ASA Data Mining Conf: A Hard Look at DMEPIA: Portuguese Conference on Artificial IntelligenceFCKAML: French Conf on Know. Acquisition & Machine LearningICANN: International Conf on Artificial Neural NetworksICCB: International Conference on Case-Based ReasoningICGA: International Conference on Genetic AlgorithmsICONIP: Intl Conf on Neural Information ProcessingIEA/AIE: Intl Conf on Ind. & Eng. Apps of AI & Expert SysICMS: International Conference on Multiagent SystemsICPS: International conference on Planning SystemsIWANN: Intl Work-Conf on Art & Natural Neural NetworksPACES: Pacific Asian Conference on Expert SystemsSCAI: Scandinavian Conference on Artifical IntelligenceSPICIS: Singapore Intl Conf on Intelligent SystemPAKDD: Pacific-Asia Conf on Know. Discovery & Data MiningSMC: IEEE Intl Conf on Systems, Man and CyberneticsPAKDDM: Practical App of Knowledge Discovery & Data MiningWCNN: The World Congress on Neural NetworksWCES: World Congress on Expert SystemsINBS: IEEE Intl Symp on Intell. in Neural \& Bio SystemsASC: Intl Conf on AI and Soft ComputingPACLIC: Pacific Asia Conference on Language, Information and Computation ICCC: International Conference on Chinese ComputingOthers:ICRA: IEEE Intl Conf on Robotics and AutomationNNSP: Neural Networks for Signal ProcessingICASSP: IEEE Intl Conf on Acoustics, Speech and SPGCCCE: Global Chinese Conference on Computers in EducationICAI: Intl Conf on Artificial IntelligenceAEN: IASTED Intl Conf on AI, Exp Sys & Neural NetworksWMSCI: World Multiconfs on Sys, Cybernetics & InformaticsAREA: Hardware and ArchitectureRank 1:ASPLOS: Architectural Support for Prog Lang and OSISCA: ACM/IEEE Symp on Computer ArchitectureICCAD: Intl Conf on Computer-Aided DesignDAC: Design Automation ConfMICRO: Intl Symp on MicroarchitectureHPCA: IEEE Symp on High-Perf Comp ArchitectureRank 2:FCCM: IEEE Symposium on Field Programmable Custom Computing Machines SUPER: ACM/IEEE Supercomputing ConferenceICS: Intl Conf on SupercomputingISSCC: IEEE Intl Solid-State Circuits ConfHCS: Hot Chips SympVLSI: IEEE Symp VLSI CircuitsISSS: International Symposium on System SynthesisDATE: IEEE/ACM Design, Automation & Test in Europe ConferenceRank 3:ICA3PP: Algs and Archs for Parall ProcEuroMICRO: New Frontiers of Information TechnologyACS: Australian Supercomputing ConfUnranked:Advanced Research in VLSIInternational Symposium on System SynthesisInternational Symposium on Computer DesignInternational Symposium on Circuits and SystemsAsia Pacific Design Automation ConferenceInternational Symposium on Physical DesignInternational Conference on VLSI DesignAREA: ApplicationsRank 1:I3DG: ACM-SIGRAPH Interactive 3D GraphicsSIGGRAPH: ACM SIGGRAPH ConferenceACM-MM: ACM Multimedia ConferenceDCC: Data Compression ConfSIGMETRICS: ACM Conf on Meas. & Modelling of Comp SysSIGIR: ACM SIGIR Conf on Information RetrievalPECCS: IFIP Intl Conf on Perf Eval of Comp \& Comm SysWWW: World-Wide Web ConferenceRank 2:EUROGRAPH: European Graphics ConferenceCGI: Computer Graphics InternationalCANIM: Computer AnimationPG: Pacific GraphicsIEEE-MM: IEEE Intl Conf on Multimedia Computing and SysNOSSDAV: Network and OS Support for Digital A/VPADS: ACM/IEEE/SCS Workshop on Parallel \& Dist Simulation WSC: Winter Simulation ConferenceASS: IEEE Annual Simulation SymposiumMASCOTS: Symp Model Analysis \& Sim of Comp \& Telecom Sys PT: Perf Tools - Intl Conf on Model Tech \& Tools for CPENetStore - Network Storage SymposiumRank 3:ACM-HPC: ACM Hypertext ConfMMM: Multimedia ModellingDSS: Distributed Simulation SymposiumSCSC: Summer Computer Simulation ConferenceWCSS: World Congress on Systems SimulationESS: European Simulation SymposiumESM: European Simulation MulticonferenceHPCN: High-Performance Computing and NetworkingGeometry Modeling and ProcessingWISEDS-RT: Distributed Simulation and Real-time ApplicationsIEEE Intl Wshop on Dist Int Simul and Real-Time ApplicationsUn-ranked:DVAT: IS\&T/SPIE Conf on Dig Video Compression Alg \& Tech MME: IEEE Intl Conf. on Multimedia in EducationICMSO: Intl Conf on Modelling, Simulation and OptimisationICMS: IASTED Intl Conf on Modelling and SimulationAREA: System TechnologyRank 1:SIGCOMM: ACM Conf on Comm Architectures, Protocols & Apps INFOCOM: Annual Joint Conf IEEE Comp & Comm SocSPAA: Symp on Parallel Algms and ArchitecturePODC: ACM Symp on Principles of Distributed ComputingPPoPP: Principles and Practice of Parallel ProgrammingMassPar: Symp on Frontiers of Massively Parallel ProcRTSS: Real Time Systems SympSOSP: ACM SIGOPS Symp on OS PrinciplesSOSDI: Usenix Symp on OS Design and ImplementationCCS: ACM Conf on Comp and Communications SecurityIEEE Symposium on Security and PrivacyMOBICOM: ACM Intl Conf on Mobile Computing and Networking USENIX Conf on Internet Tech and SysICNP: Intl Conf on Network ProtocolsOPENARCH: IEEE Conf on Open Arch and Network ProgPACT: Intl Conf on Parallel Arch and Compil TechRank 2:CC: Compiler ConstructionIPDPS: Intl Parallel and Dist Processing SympIC3N: Intl Conf on Comp Comm and NetworksICPP: Intl Conf on Parallel ProcessingICDCS: IEEE Intl Conf on Distributed Comp SystemsSRDS: Symp on Reliable Distributed SystemsMPPOI: Massively Par Proc Using Opt InterconnsASAP: Intl Conf on Apps for Specific Array ProcessorsEuro-Par: European Conf. on Parallel ComputingFast Software EncryptionUsenix Security SymposiumEuropean Symposium on Research in Computer SecurityWCW: Web Caching WorkshopLCN: IEEE Annual Conference on Local Computer NetworksIPCCC: IEEE Intl Phoenix Conf on Comp & CommunicationsCCC: Cluster Computing ConferenceICC: Intl Conf on CommRank 3:MPCS: Intl. Conf. on Massively Parallel Computing SystemsGLOBECOM: Global CommICCC: Intl Conf on Comp CommunicationNOMS: IEEE Network Operations and Management SympCONPAR: Intl Conf on Vector and Parallel ProcessingVAPP: Vector and Parallel ProcessingICPADS: Intl Conf. on Parallel and Distributed SystemsPublic Key CryptosystemsIEEE Computer Security Foundations WorkshopAnnual Workshop on Selected Areas in CryptographyAustralasia Conference on Information Security and PrivacyInt. Conf on Inofrm and Comm. SecurityFinancial CryptographyWorkshop on Information HidingSmart Card Research and Advanced Application ConferenceICON: Intl Conf on NetworksIMSA: Intl Conf on Internet and MMedia SysNCC: Nat Conf CommIN: IEEE Intell Network WorkshopICME: Intl Conf on MMedia & ExpoSoftcomm: Conf on Software in Tcomms and Comp NetworksINET: Internet Society ConfWorkshop on Security and Privacy in E-commerceUn-ranked:PARCO: Parallel ComputingSE: Intl Conf on Systems EngineeringAREA: Programming Languages and Software EngineeringRank 1:POPL: ACM-SIGACT Symp on Principles of Prog LangsPLDI: ACM-SIGPLAN Symp on Prog Lang Design & ImplOOPSLA: OO Prog Systems, Langs and ApplicationsICFP: Intl Conf on Function ProgrammingJICSLP/ICLP/ILPS: (Joint) Intl Conf/Symp on Logic ProgICSE: Intl Conf on Software EngineeringFSE: ACM Conference on the Foundations of Software Engineering (inc: ESEC-FSE when held jointly)FM/FME: Formal Methods, World Congress/EuropeCAV: Computer Aided VerificationRank 2:CP: Intl Conf on Principles & Practice of Constraint ProgTACAS: Tools and Algos for the Const and An of SystemsESOP: European Conf on ProgrammingICCL: IEEE Intl Conf on Computer LanguagesPEPM: Symp on Partial Evalutation and Prog ManipulationSAS: Static Analysis SymposiumRTA: Rewriting Techniques and ApplicationsESEC: European Software Engineering ConfIWSSD: Intl Workshop on S/W Spec & DesignCAiSE: Intl Conf on Advanced Info System EngineeringITC: IEEE Intl Test ConfIWCASE: Intl Workshop on Cumpter-Aided Software EngSSR: ACM SIGSOFT Working Conf on Software ReusabilitySEKE: Intl Conf on S/E and Knowledge EngineeringICSR: IEEE Intl Conf on Software ReuseASE: Automated Software Engineering ConferencePADL: Practical Aspects of Declarative LanguagesISRE: Requirements EngineeringICECCS: IEEE Intl Conf on Eng. of Complex Computer SystemsIEEE Intl Conf on Formal Engineering MethodsIntl Conf on Integrated Formal MethodsFOSSACS: Foundations of Software Science and Comp StructRank 3:FASE: Fund Appr to Soft EngAPSEC: Asia-Pacific S/E ConfPAP/PACT: Practical Aspects of PROLOG/Constraint TechALP: Intl Conf on Algebraic and Logic ProgrammingPLILP: Prog, Lang Implentation & Logic ProgrammingLOPSTR: Intl Workshop on Logic Prog Synthesis & TransfICCC: Intl Conf on Compiler ConstructionCOMPSAC: Intl. Computer S/W and Applications ConfCSM: Conf on Software MaintenanceTAPSOFT: Intl Joint Conf on Theory & Pract of S/W DevWCRE: SIGSOFT Working Conf on Reverse EngineeringAQSDT: Symp on Assessment of Quality S/W Dev ToolsIFIP Intl Conf on Open Distributed ProcessingIntl Conf of Z UsersIFIP Joint Int'l Conference on Formal Description Techniques and Protocol Specification, Testing, And VerificationPSI (Ershov conference)UML: International Conference on the Unified Modeling LanguageUn-ranked:Australian Software Engineering ConferenceIEEE Int. W'shop on Object-oriented Real-time Dependable Sys. (WORDS)IEEE International Symposium on High Assurance Systems EngineeringThe Northern Formal Methods WorkshopsFormal Methods PacificInt. Workshop on Formal Methods for Industrial Critical SystemsJFPLC - International French Speaking Conference on Logic and Constraint ProgrammingL&L - Workshop on Logic and LearningSFP - Scottish Functional Programming WorkshopHASKELL - Haskell WorkshopLCCS - International Workshop on Logic and Complexity in Computer ScienceVLFM - Visual Languages and Formal MethodsNASA LaRC Formal Methods Workshop(1) FATES - A Satellite workshop on Formal Approaches to Testing of Software(1) Workshop On Java For High-Performance Computing(1) DSLSE - Domain-Specific Languages for Software Engineering(1) FTJP - Workshop on Formal Techniques for Java Programs(*) WFLP - International Workshop on Functional and (Constraint) Logic Programming(*) FOOL - International Workshop on Foundations of Object-Oriented Languages(*) SREIS - Symposium on Requirements Engineering for Information Security(*) HLPP - International workshop on High-level parallel programming and applications(*) INAP - International Conference on Applications of Prolog(*) MPOOL - Workshop on Multiparadigm Programming with OO Languages(*) PADO - Symposium on Programs as Data Objects(*) TOOLS: Int'l Conf Technology of Object-Oriented Languages and Systems(*) Australasian Conference on Parallel And Real-Time SystemsAREA: Algorithms and TheoryRank 1:STOC: ACM Symp on Theory of ComputingFOCS: IEEE Symp on Foundations of Computer ScienceCOLT: Computational Learning TheoryLICS: IEEE Symp on Logic in Computer ScienceSCG: ACM Symp on Computational GeometrySODA: ACM/SIAM Symp on Discrete AlgorithmsSPAA: ACM Symp on Parallel Algorithms and ArchitecturesPODC: ACM Symp on Principles of Distributed ComputingISSAC: Intl. Symp on Symbolic and Algebraic ComputationCRYPTO: Advances in CryptologyEUROCRYPT: European Conf on CryptographyRank 2:CONCUR: International Conference on Concurrency TheoryICALP: Intl Colloquium on Automata, Languages and ProgSTACS: Symp on Theoretical Aspects of Computer ScienceCC: IEEE Symp on Computational ComplexityWADS: Workshop on Algorithms and Data StructuresMFCS: Mathematical Foundations of Computer ScienceSWAT: Scandinavian Workshop on Algorithm TheoryESA: European Symp on AlgorithmsIPCO: MPS Conf on integer programming & comb optimization LFCS: Logical Foundations of Computer ScienceALT: Algorithmic Learning TheoryEUROCOLT: European Conf on Learning TheoryWDAG: Workshop on Distributed AlgorithmsISTCS: Israel Symp on Theory of Computing and SystemsISAAC: Intl Symp on Algorithms and ComputationFST&TCS: Foundations of S/W Tech & Theoretical CSLATIN: Intl Symp on Latin American Theoretical InformaticsRECOMB: Annual Intl Conf on Comp Molecular BiologyCADE: Conf on Automated DeductionIEEEIT: IEEE Symposium on Information TheoryAsiacryptRank 3:MEGA: Methods Effectives en Geometrie AlgebriqueASIAN: Asian Computing Science ConfCCCG: Canadian Conf on Computational GeometryFCT: Fundamentals of Computation TheoryWG: Workshop on Graph TheoryCIAC: Italian Conf on Algorithms and ComplexityICCI: Advances in Computing and InformationAWTI: Argentine Workshop on Theoretical InformaticsCATS: The Australian Theory SympCOCOON: Annual Intl Computing and Combinatorics ConfUMC: Unconventional Models of ComputationMCU: Universal Machines and ComputationsGD: Graph DrawingSIROCCO: Structural Info & Communication ComplexityALEX: Algorithms and ExperimentsALG: ENGG Workshop on Algorithm EngineeringLPMA: Intl Workshop on Logic Programming and Multi-Agents EWLR: European Workshop on Learning RobotsCITB: Complexity & info-theoretic approaches to biologyFTP: Intl Workshop on First-Order Theorem Proving (FTP)CSL: Annual Conf on Computer Science Logic (CSL)AAAAECC: Conf On Applied Algebra, Algebraic Algms & ECC DMTCS: Intl Conf on Disc Math and TCSUn-ranked:Information Theory WorkshopAREA: Data BasesRank 1:SIGMOD: ACM SIGMOD Conf on Management of DataPODS: ACM SIGMOD Conf on Principles of DB SystemsVLDB: Very Large Data BasesICDE: Intl Conf on Data EngineeringICDT: Intl Conf on Database TheoryRank 2:SSD: Intl Symp on Large Spatial DatabasesDEXA: Database and Expert System ApplicationsFODO: Intl Conf on Foundation on Data OrganizationEDBT: Extending DB TechnologyDOOD: Deductive and Object-Oriented DatabasesDASFAA: Database Systems for Advanced ApplicationsCIKM: Intl. Conf on Information and Knowledge ManagementSSDBM: Intl Conf on Scientific and Statistical DB MgmtCoopIS - Conference on Cooperative Information SystemsER - Intl Conf on Conceptual Modeling (ER)Rank 3:COMAD: Intl Conf on Management of DataBNCOD: British National Conference on DatabasesADC: Australasian Database ConferenceADBIS: Symposium on Advances in DB and Information SystemsDaWaK - Data Warehousing and Knowledge DiscoveryRIDE WorkshopIFIP-DS: IFIP-DS ConferenceIFIP-DBSEC - IFIP Workshop on Database SecurityNGDB: Intl Symp on Next Generation DB Systems and AppsADTI: Intl Symp on Advanced DB Technologies and IntegrationFEWFDB: Far East Workshop on Future DB SystemsMDM - Int. Conf. on Mobile Data Access/Management (MDA/MDM)ICDM - IEEE International Conference on Data MiningVDB - Visual Database SystemsIDEAS - International Database Engineering and Application SymposiumOthers:ARTDB - Active and Real-Time Database SystemsCODAS: Intl Symp on Cooperative DB Systems for Adv AppsDBPL - Workshop on Database Programming LanguagesEFIS/EFDBS - Engineering Federated Information (Database) SystemsKRDB - Knowledge Representation Meets DatabasesNDB - National Database Conference (China)NLDB - Applications of Natural Language to Data BasesKDDMBD - Knowledge Discovery and Data Mining in Biological Databases Meeting FQAS - Flexible Query-Answering SystemsIDC(W) - International Database Conference (HK CS)RTDB - Workshop on Real-Time DatabasesSBBD: Brazilian Symposium on DatabasesWebDB - International Workshop on the Web and DatabasesWAIM: Interational Conference on Web Age Information Management(1) DASWIS - Data Semantics in Web Information Systems(1) DMDW - Design and Management of Data Warehouses(1) DOLAP - International Workshop on Data Warehousing and OLAP(1) DMKD - Workshop on Research Issues in Data Mining and Knowledge Discovery (1) KDEX - Knowledge and Data Engineering Exchange Workshop(1) NRDM - Workshop on Network-Related Data Management(1) MobiDE - Workshop on Data Engineering for Wireless and Mobile Access(1) MDDS - Mobility in Databases and Distributed Systems(1) MEWS - Mining for Enhanced Web Search(1) TAKMA - Theory and Applications of Knowledge MAnagement(1) WIDM: International Workshop on Web Information and Data Management(1) W2GIS - International Workshop on Web and Wireless Geographical Information Systems * CDB - Constraint Databases and Applications* DTVE - Workshop on Database Technology for Virtual Enterprises* IWDOM - International Workshop on Distributed Object Management* IW-MMDBMS - Int. Workshop on Multi-Media Data Base Management Systems* OODBS - Workshop on Object-Oriented Database Systems* PDIS: Parallel and Distributed Information SystemsAREA: MiscellaneousRank 1:Rank 2:AMIA: American Medical Informatics Annual Fall SymposiumDNA: Meeting on DNA Based ComputersRank 3:MEDINFO: World Congress on Medical InformaticsInternational Conference on Sequences and their ApplicationsECAIM: European Conf on AI in MedicineAPAMI: Asia Pacific Assoc for Medical Informatics ConfSAC: ACM/SIGAPP Symposium on Applied ComputingICSC: Internal Computer Science ConferenceISCIS: Intl Symp on Computer and Information SciencesICSC2: International Computer Symposium ConferenceICCE: Intl Conf on Comps in EduEd-MediaWCC: World Computing CongressPATAT: Practice and Theory of Automated TimetablingNot Encouraged (due to dubious referee process):International Multiconferences in Computer Science -- 14 joint int'l confs.SCI: World Multi confs on systemics, sybernetics and informaticsSSGRR: International conf on Advances in Infrastructure for e-B, e-Edu and e-Science and e-MedicineIASTED conferences以下是期刊：IEEE/ACM TRANSACTIONS期刊系列一般都被公认为领域顶级期刊，所以以下列表在关于IEEE/ACM TRANSACTIONS的分类不一定太准确。
18_Parallel Processing
• Now have multiple processors as well as multiple I/O modules
Symmetric Multiprocessor Organization
Time Share Bus - Advantages
• Simplicity • Flexibility • Reliability
• Performance
—If some work can be done in parallel
• Availability
—Since all processors can perform the same functions, failure of a single processor does not halt the system
• L2 cache 32 MB
— Clusters of five — Each cluster supports eight processors and access to entire main memory space
• System control element (SCE)
— Arbitrates system communication — Maintains cache coherence
Taxonomy of Parallel Processor Architectures
MIMD - Overview
• General purpose processors • Each can process all instructions necessary • Further classified by method of processor communication
Signal and Image Processing
Signal and Image Processing Signal and image processing are two of the most important fields in modern technology. They play a crucial role in a variety of applications, ranging from communication systems and medical imaging to security and surveillance. Signal processing involves the analysis, manipulation, and transformation of signals, which can be in the form of sound, images, or other types of data. Image processing, on the other hand, is focused on the analysis and manipulation ofdigital images, which are composed of pixels or small picture elements. One ofthe key challenges in signal and image processing is dealing with noise and other forms of interference. Noise can be introduced into a signal or image through a variety of sources, such as electromagnetic interference, thermal noise, or quantization errors. To address this challenge, signal and image processing techniques have been developed that can reduce or eliminate noise from a signal or image. For example, filtering techniques can be used to remove noise from a signal, while image enhancement techniques can be used to improve the quality of an image by reducing noise and increasing contrast. Another important aspect of signal and image processing is feature extraction. Feature extraction involves identifyingand isolating specific features or characteristics of a signal or image that are relevant to a particular application. For example, in medical imaging, feature extraction techniques can be used to identify tumors or other abnormalities in an image. In security and surveillance applications, feature extraction techniquescan be used to identify specific individuals or objects in a video stream. Machine learning is also becoming increasingly important in signal and image processing. Machine learning algorithms can be used to automatically learn and adapt to patterns in signals and images, allowing for more efficient and accurate processing. For example, machine learning algorithms can be used to classify different types of signals or images, or to detect anomalies or other unusual patterns. One of the challenges of signal and image processing is the sheer amount of data that is generated. Modern sensors and cameras can produce vast amounts of data, which can be difficult to process and analyze in real-time. To address this challenge, techniques such as compression and data reduction can be used to reduce the amount of data that needs to be processed. Additionally,parallel processing and distributed computing techniques can be used to speed up the processing of large amounts of data. In conclusion, signal and image processing are essential fields in modern technology. They play a critical role in a wide range of applications, from communication systems and medical imaging to security and surveillance. Signal and image processing techniques are constantly evolving, driven by advances in technology and the increasing demand for more efficient and accurate processing. As the amount of data generated by sensors and cameras continues to grow, new techniques will be needed to address the challenges of processing and analyzing this data in real-time.。
香港理工大学高分辨率的指纹（HRF）数据库_图像处理_科研数据集
⾹港理⼯⼤学⾼分辨率的指纹（HRF）数据库_图像处理_科研数据集⾹港理⼯⼤学⾼分辨率的指纹(HRF) 数据库(The Hong Kong Polytechnic University(PolyU)High-Resolution-Fingerprint (HRF)Database)数据介绍：Fingerprint is the most widely used biometric characteristic for personal identification because of its uniqueness and stability over time. Most of the existing automatic fingerprint recognition systems (AFRS) use the minutia features on fingerprints, i.e. the terminations and bifurcations of fingerprint ridges, for recognition. Although they can achieve good recognition accuracy and have been used in many civil applications, their performance still needs much improvement when a large population is involved or a high security level is required. One solution to enhancing the accuracy of AFRS is to employ more features on fingerprints other than only minutiae. Fingerprint additional features, such as pores, dots and incipient ridges (see Fig. 1 for examples), are routinely used by experts in manual latent fingerprint matching. Some of these additional features, e.g. pores, require high resolution fingerprint images to reliably capture them. Thanks to the distinctiveness of these fingerpr关键词：⾼分辨率的指纹,⾹港理⼯⼤学,UGC/CRC,High-Resolution-Fingerprint,PolyU,UGC/CRC,数据格式：IMAGE数据详细介绍：The Hong Kong Polytechnic University (PolyU)High-Resolution-Fingerprint (HRF) DatabaseOverview:Fingerprint is the most widely used biometric characteristic for personal identification because of its uniqueness and stability over time. Most of the existing automatic fingerprint recognition systems (AFRS) use the minutia features on fingerprints, i.e. the terminations and bifurcations of fingerprint ridges, for recognition. Although they can achieve good recognition accuracy and have been used in many civil applications, their performance still needs much improvement when a large population is involved or a high security level is required. One solution to enhancing the accuracy of AFRS is to employ more features on fingerprints other than only minutiae. Fingerprint additional features, such as pores, dots and incipient ridges (see Fig. 1 for examples), are routinely used by experts in manual latent fingerprint matching. Some of these additional features, e.g. pores, require high resolution fingerprint images to reliably capture them. Thanks to the distinctiveness of these fingerprint additional features and to the advent of high quality fingerprint imaging sensors, they have recently attracted increasing attention from researchers and practitioners working on AFRS.Our team in the Biometrics Research Centre (UGC/CRC) of the HongKong Polytechnic University has developed a high resolution fingerprintimaging device and has used it to constructed large-scale high resolutionfingerprint databases (HRF). We intend to publish our database to facilitate researchers designing effective and efficient algorithms for extracting andmatching fingerprint additional features.Fig. 1: Example additional features on fingerprints, pores, dots, and incipientridges.Description of the PolyU HRF Database:An optical fingerprint imaging device (see Fig. 2) has been built by our team.Its resolution is around 1,200dpi, and it can capture fingerprint images ofvarious sizes, e.g. 320*240 pixels and 640*480 pixels.(a) (b)Fig. 2: (a) The high resolution fingerprint imaging device we developed and (b) its inner structure.Two high resolution fingerprint image databases (denoted as DBI and DBII)have been set up by using the developed fingerprint imaging device. DBIconsists of a small training dataset and a large test dataset. The images of thesame finger in both databases were collected in two sessions which wereseparated by about two weeks. Each image is namedas “ID_S_X”.“ID” represents the identity of the person. “S” represents the session of the captured image. “X”represents the image number of each session. The following table gives the detailed information of the databases.We labeled the ground truth of sweat pores in 30 images selected from DBI. The central coordinates (row, col) of each pore were wrote into a text file (.txt). The ground truth of dots and incipients of 48 selected images were also offered. The central coordinates (row, col) of dots and two ends of each incipient were wrote into a text file (.txt). Here, the 48 selected images consists of 2 set of images ("SetIGroundTruthSampleimage" and "SetIIGroundTruthSampleimage") captured in two sessions. All of the original sample images and text files are contained in "Ground Truth.zip".Related Publication:1. Qijun Zhao, David Zhang, Lei Zhang, and Nan Luo, "AdaptiveFingerprint Pore Modeling and Extraction," Pattern Recognition, vol.43(8), pp. 2833-2844, 20102. Qijun Zhao, David Zhang, Lei Zhang, and Nan Luo, "High ResolutionFragmentary Fingerprint Alignment Using Pore-Valley Descriptors,"Pattern Recognition, vol. 43, pp. 1050-1061, 20103. Qijun Zhao, Lei Zhang, David Zhang, Nan Luo, and Jing Bao,“Adaptive Pore Model for Fingerprint Pore Extraction,” IAPR 19thInternational Conference on Pattern Recognition (ICPR2008), 20084. Qijun Zhao, Lei Zhang, David Zhang, and Nan Luo, “Direct PoreMatching for Fingerprint Recognition,” IAPR/IEEE 3rd InternationalConference on Biometrics (ICB2009), pp. 597-606, 20095. David Zhang, Feng Liu, Qijun Zhao, Guangming Lu, and Nan Luo,"Selecting a Reference High Resolution for Fingerprint RecognitionUsing Minutiae and Pores," IEEE Transactions on Instrumentation and Measurement, to appear6. Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, "A ComparativeStudy on Quality Assessment of High Resolution Fingerprint Images,"Proceedings of the IEEE International Conference on Image Processing (ICIP2010), Hong Kong, September 20107. Qijun Zhao, Feng Liu, Lei Zhang, and David Zhang, "Parallel versusHierarchical Fusion of Extended Fingerprint Features," Proceedings ofthe IAPR 20th International Conference on Pattern Recognition(ICPR'10), Istanbul, Turkey, August 20108. Feng Liu, Qijun Zhao, Lei Zhang, and David Zhang, "Fingerprint PoreMatching based on Sparse Representation," Proceedings of the IAPR20th International Conference on Pattern Recognition(ICPR'10), Istanbul, Turkey, August 20109. Q. Zhao, Lei Zhang, David Zhang, Wenyi Huang, and Jian Bai,“Curvature and Singularity Driven Diffusion for Oriented PatternEnhancement with Singular Points,” CVPR09. Proceedings of IEEEConference on Computer Vision and Pattern Recognition, pp. 1-7,Miami, Florida, USA, June 22-24 2009.The Announcement of the CopyrightAll rights of the PolyU HRF Database are reserved. The database is only available for research and noncommercial purposes. Commercial distribution or any act related to commercial use of this database is strictly prohibited. A clear acknowledgement should be made for any public work based on the PolyU HRF Database. A citation to "PolyU HRF Database, /doc/399f7c6fa45177232f60a2bf.html .hk/~biometrics/HRF/HRF.htm” and our related works must be added in the references. A soft copy of any released or public documents that use the PolyU HRF Database must be forwardedto: cslzhang@/doc/399f7c6fa45177232f60a2bf.html .hkDownloading Steps:Download “HRF DBI.zip”, “HRF DBII.zip”, or "Ground Truth.zip"to your local disk. Then, fill in the application forms. Send the application formto cslzhang@/doc/399f7c6fa45177232f60a2bf.html .hk. The successful applicants will receive the passwords for unzipping the files downloaded.HRF Databases:HRF DBI.zipHRF DBII.zipGround Truth.zipContact Information:Lei ZHANG, Associate ProfessorBiometric Research Centre (UGC/CRC)The Hong Kong Polytechnic UniversityHung Hom, Kowloon, Hong KongE-mail: cslzhang@/doc/399f7c6fa45177232f60a2bf.html .hk数据预览：点此下载完整数据集。
图像处理领域公认的重要英文期刊和会议分级
人工智能和图像处理方面的各种会议的评级2010年8月31日忙菇发表评论阅读评论人工智能和图像处理方面的各种会议的评级澳大利亚政府和澳大利亚研究理事会做的，有一定参考价值会议名称会议缩写评级ACM SIG International Conference on Computer Graphics and Interactive Techniques SIGGRAPH AACM Virtual Reality Software and Technology VRST AACM/SPIE Multimedia Computing and Networking MMCN AACM-SIGRAPH Interactive 3D Graphics I3DG AAdvances in Neural Information Processing Systems NIPS AAnnual Conference of the Cognitive Science Society CogSci AAnnual Conference of the International Speech Communication Association (was Eurospeech) Interspeech AAnnual Conference on Computational Learning Theory COLT AArtificial Intelligence in Medicine AIIM AArtificial Intelligence in Medicine in Europe AIME AAssociation of Computational Linguistics ACL ACognitive Science Society Annual Conference CSSAC AComputer Animation CANIM AConference in Uncertainty in Artificial Intelligence UAI AConference on Natural Language Learning CoNLL AEmpirical Methods in Natural Language Processing EMNLP AEuropean Association of Computational Linguistics EACL AEuropean Conference on Artificial Intelligence ECAI AEuropean Conference on Computer Vision ECCV AEuropean Conference on Machine Learning ECML AEuropean Conference on Speech Communication and Technology (now Interspeech) EuroSpeech AEuropean Graphics Conference EUROGRAPH AFoundations of Genetic Algorithms FOGA AIEEE Conference on Computer Vision and Pattern Recognition CVPR AIEEE Congress on Evolutionary Computation IEEE CEC AIEEE Information Visualization Conference IEEE InfoVis AIEEE International Conference on Computer Vision ICCV AIEEE International Conference on Fuzzy Systems FUZZ-IEEE AIEEE International Joint Conference on Neural Networks IJCNN AIEEE International Symposium on Artificial Life IEEE Alife AIEEE Visualization IEEE VIS AIEEE Workshop on Applications of Computer Vision WACV AIEEE/ACM International Conference on Computer-Aided Design ICCAD AIEEE/ACM International Symposium on Mixed and Augmented Reality ISMAR A International Conference on Automated Deduction CADE AInternational Conference on Autonomous Agents and Multiagent Systems AAMAS A International Conference on Computational Linguistics COLING AInternational Conference on Computer Graphics Theory and Application GRAPP A International Conference on Intelligent Tutoring Systems ITS AInternational Conference on Machine Learning ICML AInternational Conference on Neural Information Processing ICONIP AInternational Conference on the Principles of Knowledge Representation and Reasoning KR A International Conference on the Simulation and Synthesis of Living Systems ALIFE A International Joint Conference on Artificial Intelligence IJCAI AInternational Joint Conference on Automated Reasoning IJCAR AInternational Joint Conference on Qualitative and Quantitative Practical Reasoning ESQARU A Medical Image Computing and Computer-Assisted Intervention MICCAI ANational Conference of the American Association for Artificial Intelligence AAAI ANorth American Association for Computational Linguistics NAACL APacific Conference on Computer Graphics and Applications PG AParallel Problem Solving from Nature PPSN AACM SIGGRAPH/Eurographics Symposium on Computer Animation SCA BAdvanced Concepts for Intelligent Vision Systems ACIVS BAdvanced Visual Interfaces AVI BAgent-Oriented Information Systems Workshop AOIS BAnnual International Workshop on Presence PRESENCE BArtificial Neural Networks in Engineering Conference ANNIE BAsian Conference on Computer Vision ACCV BAsia-Pacific Conference on Simulated Evolution and Learning SEAL BAustralasian Conference on Robotics and Automation ACRA BAustralasian Joint Conference on Artificial Intelligence AI BAustralasian Speech Science and Technology S ST BAustralian Conference for Knowledge Management and Intelligent Decision Support A CKMIDS B Australian Conference on Artificial Life ACAL BAustralian Symposium on Information Visualisation ASIV BBritish Machine Vision Conference B MVC BCanadian Artificial Intelligence Conference CAAI BComputer Graphics International CGI BConference of the Association for Machine Translation in the Americas AMTA B Conference of the European Association for Machine Translation EAMT BConference of the Pacific Association for Computational Linguistics PACLING BConference on Artificial Intelligence for Applications CAIA BCongress of the Italian Assoc for AI AI*IA BDeutsche Arbeitsgemeinschaft für Mustererkennung DAGM e.V DAGM BDigital Image Computing Techniques and Applications DICTA BEurographics Symposium on Parallel Graphics and Visualization EGPGV BEurographics/IEEE Symposium on Visualization EuroVis BEuropean Conference on Artificial Life ECAL BEuropean Conference on Genetic Programming EUROGP BEuropean Simulation Symposium ESS BEuropean Symposium on Artificial Neural Networks ESANN BFrench Conference on Knowledge Acquisition and Machine Learning FCKAML BGerman Conference on Multi-Agent system Technologies MATES BGraphics Interface GI BIEEE International Conference on Image Processing ICIP BIEEE International Conference on Multimedia and Expo ICME BIEEE International Conference on Neural Networks ICNN BIEEE International Workshop on Visualizing Software for Understanding and Analysis VISSOFT BIEEE Pacific Visualization Symposium (was APVIS) PacificVis BIEEE Symposium on 3D User Interfaces 3DUI BIEEE Virtual Reality Conference VR BIFSA World Congress IFSA BImage and Vision Computing Conference IVCNZ BInnovative Applications in AI IAAI BIntegration of Software Engineering and Agent Technology ISEAT BIntelligent Virtual Agents IVA BInternational Cognitive Robotics Conference COGROBO BInternational Conference on Advances in Intelligent Systems: Theory and Applications AISTABInternational Conference on Artificial Intelligence and Statistics AISTATS BInternational Conference on Artificial Neural Networks ICANN BInternational Conference on Artificial Reality and Telexistence ICAT BInternational Conference on Computer Analysis of Images and Patterns CAIP BInternational Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia S IGGRAPH ASIA BInternational Conference on Database and Expert Systems Applications DEXA B International Conference on Frontiers of Handwriting Recognition ICFHR BInternational Conference on Genetic Algorithms ICGA BInternational Conference on Image Analysis and Processing ICIAP BInternational Conference on Implementation and Application of Automata CIAA B International Conference on Information Visualisation IV BInternational Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming for Combinatorial Optimization Problems CPAIOR B International Conference on Intelligent Systems and Knowledge Engineering ISKE B International Conference on Intelligent Text Processing and Computational Linguistics CICLING BInternational Conference on Knowledge Science, Engineering and Management KSEM B International Conference on Modelling Decisions for Artificial Intelligence MDAI B International Conference on Multiagent Systems ICMS BInternational Conference on Pattern Recognition ICPR BInternational Conference on Software Engineering and Knowledge Engineering SEKE B International Conference on Theoretical and Methodological Issues in machine Translation TMI BInternational Conference on Tools with Artificial Intelligence ICTAI BInternational Conference on Ubiquitous and Intelligence Computing UIC BInternational Conference on User Modelling (now UMAP) UM BInternational Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision WSCG BInternational Fuzzy Logic and Intelligent technologies in Nuclear Science Conference F LINS B International Joint Conference on Natural Language Processing IJCNLP BInternational Meeting on DNA Computing and Molecular Programming DNA BInternational Natural Language Generation Conference INLG BInternational Symposium on Artificial Intelligence and Maths ISAIM BInternational Symposium on Computational Life Science CompLife BInternational Symposium on Mathematical Morphology ISMM BInternational Work-Conference on Artificial and Natural Neural Networks IWANN B International Workshop on Agents and Data Mining Interaction ADMI BInternational Workshop on Ant Colony ANTS BInternational Workshop on Paraphrasing IWP BInternational Workshops on Enabling Technologies: Infrastructures for Collaborative Enterprises WETICE BJoint workshop on Multimodal Interaction and Related Machine Learning Algorithms (nowICMI-MLMI) MLMI BLogic and Engineering of Natural Language Semantics LENLS BMachine Translation Summit MT SUMMIT BPacific Asia Conference on Language, Information and Computation PACLIC BPacific Asian Conference on Expert Systems PACES BPacific Rim International Conference on Artificial Intelligence PRICAI BPacific Rim International Workshop on Multi-Agents PRIMA BPacific-Rim Symposium on Image and Video Technology PSIVT BPortuguese Conference on Artificial Intelligence EPIA BRobot Soccer World Cup RoboCup BScandinavian Conference on Artificial Intelligence S CAI BSingapore International Conference on Intelligent Systems SPICIS BSPIE International Conference on Visual Communications and Image Processing VCIP B Summer Computer Simulation Conference SCSC BSymposium on Logical Formalizations of Commonsense Reasoning COMMONSENSE B The Theory and Application of Diagrams DIAGRAMS BWinter Simulation Conference WSC BWorld Congress on Expert Systems WCES BWorld Congress on Neural Networks WCNN B3-D Digital Imaging and Modelling 3DIM CACM Workshop on Secure Web Services SWS CAdvanced Course on Artificial Intelligence ACAI CAdvances in Intelligent Systems AIS CAgent-Oriented Software Engineering Workshop AOSE CAmbient Intelligence Developments Aml.d CAnnual Conference on Evolutionary Programming EP CApplications of Information Visualization IV-App CApplied Perception in Graphics and Visualization APGV CArgentine Symposium on Artificial Intelligence ASAI CArtificial Intelligence in Knowledge Management AIKM CAsia-Pacific Conference on Complex Systems C omplex CAsia-Pacific Symposium on Visualisation APVIS CAustralasian Cognitive Science Society Conference AuCSS CAustralia-Japan Joint Workshop on Intelligent and Evolutionary Systems AJWIES C Australian Conference on Neural Networks ACNN CAustralian Knowledge Acquisition Workshop AKAW CAustralian MADYMO Users Meeting MADYMO CBioinformatics Visualization BioViz CBrazilian Symposium on Computer Graphics and Image Processing SIBGRAPI C Canadian Conference on Computer and Robot Vision CRV CComplex Objects Visualization Workshop COV CComputer Animation, Information Visualisation, and Digital Effects CAivDE C Conference of the International Society for Decision Support Systems I SDSS C Conference on Artificial Neural Networks and Expert systems ANNES CConference on Visualization and Data Analysis VDA CCooperative Design, Visualization, and Engineering CDVE CCoordinated and Multiple Views in Exploratory Visualization CMV CCultural Heritage Knowledge Visualisation CHKV CDesign and Aesthetics in Visualisation DAViz CDiscourse Anaphora and Anaphor Resolution Colloquium DAARC CENVI and IDL Data Analysis and Visualization Symposium VISualize CEuro Virtual Reality Euro VR CEuropean Conference on Ambient Intelligence AmI CEuropean Conference on Computational Learning Theory (Now in COLT) EuroCOLT C European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty ECSQARU CEuropean Congress on Intelligent Techniques and Soft Computing EUFIT CEuropean Workshop on Modelling Autonomous Agents in a Multi-Agent World MAAMAW C European Workshop on Multi-Agent Systems EUMAS CFinite Differences-Finite Elements-Finite Volumes-Boundary Elements F-and-B CFlexible Query-Answering Systems FQAS CFlorida Artificial Intelligence Research Society Conference FlAIRS CFrench Speaking Conference on the Extraction and Management of Knowledge EGC C GeoVisualization and Information Visualization GeoViz CGerman Conference on Artificial Intelligence K I CHellenic Conference on Artificial Intelligence S ETN CHungarian National Conference on Agent Based Computation HUNABC CIberian Conference on Pattern Recognition and Image Analysis IBPRIA CIberoAmerican Congress on Pattern Recognition CIARP CIEEE Automatic Speech Recognition and Understanding Workshop ASRU CIEEE International Conference on Adaptive and Intelligent Systems ICAIS CIEEE International Conference on Automatic Face and Gesture Recognition FG CIEEE International Conference on Cognitive Informatics ICCI CIEEE International Conference on Computational Cybernetics ICCC CIEEE International Conference on Computational Intelligence for Measurement Systems and Applications CIMSA CIEEE International Conference on Cybernetics and Intelligent Systems CIS CIEEE International Conference on Granular Computing GrC CIEEE International Conference on Information and Automation IEEE ICIA CIEEE International Conference on Intelligence for Homeland Security and Personal Safety CIHSPS CIEEE International Conference on Intelligent Computer Communication and Processing ICCP C IEEE International Conference on Intelligent Systems IEEE IS CIEEE International Geoscience and Remote Sensing Symposium IGARSS CIEEE International Symposium on Multimedia ISM CIEEE International Workshop on Cellular Nanoscale Networks and Applications CNNA CIEEE International Workshop on Neural Networks for Signal Processing NNSP CIEEE Swarm Intelligence Symposium IEEE SIS CIEEE Symposium on Computational Intelligence and Data Mining IEEE CIDM CIEEE Symposium on Computational Intelligence and Games CIG CIEEE Symposium on Computational Intelligence for Financial Engineering IEEE CIFEr C IEEE Symposium on Computational intelligence for Image Processing IEEE CIIP CIEEE Symposium on Computational intelligence for Multimedia Signal and Vision Processing IEEE CIMSVP CIEEE Symposium on Computational Intelligence for Security and Defence Applications IEEE CISDA CIEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology IEEE CIBCB CIEEE Symposium on Computational Intelligence in Control and Automation IEEE CICA C IEEE Symposium on Computational Intelligence in Cyber Security IEEE CICS CIEEE Symposium on Computational Intelligence in Image and Signal Processing CIISP C IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making IEEE MCDM CIEEE Symposium on Computational Intelligence in Scheduling IEEE CI-Sched CIEEE Symposium on Intelligent Agents IEEE IA CIEEE Workshop on Computational Intelligence for Visual Intelligence IEEE CIVI CIEEE Workshop on Computational Intelligence in Aerospace Applications IEEE CIAA CIEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications IEEE CIB CIEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems IEEE CIWS CIEEE Workshop on Computational Intelligence in Virtual Environments IEEE CIVE CIEEE Workshop on Evolvable and Adaptive Hardware IEEE WEAH CIEEE Workshop on Evolving and Self-Developing Intelligent Systems IEEE ESDIS CIEEE Workshop on Hybrid Intelligent Models and Applications IEEE HIMA CIEEE Workshop on Memetic Algorithms IEEE WOMA CIEEE Workshop on Organic Computing IEEE OC CIEEE Workshop on Robotic Intelligence in Informationally Structured Space IEEE RiiSS C IEEE Workshop on Speech Coding SCW CIEEE/WIC/ACM International Conference on Intelligent Agent Technology IAT CIEEE/WIC/ACM international Conference on Web Intelligence and Intelligent Agent Technology WI-IAT CIFIP Conference on Biologically Inspired Collaborative Computing BICC CInformation Visualisation Theory and Practice InfVis CInformation Visualization Evaluation IVE CInformation Visualization in Biomedical Informatics IVBI CIntelligence Tools, Data Mining, Visualization IDV CIntelligent Multimedia, Video and Speech Processing Symposium MVSP C International Atlantic Web Intelligence Conference AWIC CInternational Colloquium on Data Sciences, Knowledge Discovery and Business Intelligence DSKDB CInternational Conference Computer Graphics, Imaging and Visualization CGIV CInternational Conference Formal Concept Analysis Conference ICFCA CInternational Conference Imaging Science, Systems and Technology CISST CInternational Conference on 3G Mobile Communication Technologies 3G CInternational Conference on Adaptive and Natural Computing Algorithms ICANNGA C International Conference on Advances in Pattern Recognition and Digital Techniques ICAPRDT CInternational Conference on Affective Computing and Intelligent A CII CInternational Conference on Agents and Artificial Intelligence ICAART CInternational Conference on Artificial Intelligence I C-AI CInternational Conference on Artificial Intelligence and Law ICAIL CInternational Conference on Artificial Intelligence and Pattern Recognition A IPR CInternational Conference on Artificial Intelligence and Soft Computing ICAISC C International Conference on Artificial Intelligence in Science and Technology AISAT C International Conference on Arts and Technology ArtsIT CInternational Conference on Case-Based Reasoning Research and Development ICCBR C International Conference on Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems ICCCI CInternational Conference on Computational Intelligence and Multimedia ICCIMA C International Conference on Computational Intelligence and Software Engineering CISE C International Conference on Computational Intelligence for Modelling, Control and Automation CIMCA CInternational Conference on Computational Intelligence, Robotics and Autonomous Systems CIRAS CInternational Conference on Computational Semiotics for Games and New Media Cosign C International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa AFRIGRAPH CInternational Conference on Computer Theory and Applications ICCTA CInternational Conference on Computer Vision Systems I CVS CInternational Conference on Cybercrime Forensics Education and Training CFET CInternational Conference on Engineering Applications of Neural Networks EANN C International Conference on Evolutionary Computation ICEC CInternational Conference on Fuzzy Systems and Knowledge FSKD CInternational Conference on Hybrid Artificial Intelligence Systems HAIS CInternational Conference on Hybrid Intelligent Systems HIS CInternational Conference on Image and Graphics ICIG CInternational Conference on Image and Signal Processing ICISP CInternational Conference on Immersive Telecommunications IMMERSCOM CInternational Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA/AIE CInternational Conference on Information and Knowledge Engineering I KE CInternational Conference on Intelligent Systems ICIL CInternational Conference on Intelligent Systems Designs and Applications ISDA 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已发表的学术论文
(1)Ying Lungan, Zhou Tie, "Long-time Asymptotic Behavior of Lax-Friedrichs Scheme", J. PartialDiff. Eqs., Vol.6, No.1 (1993), 39-61(2)Long-an Ying, Tie Zhou, "Nonlinear Stability of Discrete Shocks", Proceedings of the Japan-ChinaSeminar on Numerical mathematics, Lecture Notes in Numerical and Applied Analysis, Vol.14 (1995), pp227-239.(3)Tie Zhou, "A Remark on the Nonlinear Stability of Discrete Shocks for Single HyperbolicConservation Laws", Acta Scientiarum Naturalium Universitatis Pekinensis, Vol.33, No.5 (1997), pp554-560. (EI)(4)Han-song Tang, Tie Zhou, "Why nonconservative interface algorithms may be applicable: analysisfor Chimera grids", Proceedings of 7th International Symposium on CFD(1997), pp336-341. (5)Han-song Tang, Tie Zhou, "On Nonconservative Conditions at Grid Interfaces", SIAM Journal onNumerical Analysis, V ol.37, No.1 (1999), pp173-193. (SCI)(6)李荫藩，宋松和，周铁，“双曲型守恒律的高阶、高精度有限体积法”，力学进展, V ol. 31,No.2 (2001), pp245-263.(7)Tie Zhou, Yin-fan Li, Chi-Wang Shu, "Numerical Comparison of WENO Finite Volume and Runge-Kutta Discontinous Galerkin Methods", Journal of Scientific Computing, Vol.16, No.2 (2001), pp145-171. (EI)(8)Tie Zhou, Yan Guo, Chi-Wang Shu, "Numerical Study on Landau Damping", Physica D, V ol.157(2001), pp322-333. (SCI)(9)Tie Zhou, Shulin Zhou, Ming Jiang, Danfeng Lu, "Theory of continuous multiscale homomorphicfiltering and applications", XI-th International Congress for Sterology Beijing Conference, inconjunction with Xth Chinese National Symposium for Sterology and Image Analysis, November 4-8, 2003, Beijing.(10)Yan Guo, Chi-Wang Shu, Tie Zhou, "The dynamics of a Plane Diode", SIAM Journal onMathematical Analysis, V ol. 35, Number 6 pp. 1617-1635. 2004. (SCI)(11)Jinghua Wang, Hairui Wen, Tie Zhou, "On large time step schemes for hyperbolic conservationlaws", COMM. MATH. SCI. , V ol. 2, No. 3, pp. 477-495. 2004.(12)Jiansheng Yang, Qiang Kong, Tie zhou, Ming Jiang, "Cone Beam Cover Method: An Approach toPerforming Backprojection in Katsevich’s Exact Algorithm for Spiral Cone Beam CT", Journal of X-ray Science and Technology，V ol.9 , 1-16, 2004. (EI)(13)蔚喜军，周铁，“流体力学方程的间断有限元方法”，计算物理，V ol. 22, No. 2 (2005), pp108-116. (EI)(14)DanFeng Lu, HongKai Zhao, Ming Jiang, ShuLin Zhou, Tie Zhou, "A Surface ReconstructionMethod for Highly Noisy Point Clouds", N. Paragios et al. (Eds.): VLSM 2005, LNCS 3752, pp.283–294. Springer-Verlag Berlin Heidelberg 2005. (SCI)(15)郭晓虎，杨建生，孔强，周铁，姜明，“锥束覆盖方法的并行实现及性能分析”，中国体视学与图像分析，V ol.10, No.3, 165-169, 2005.(16)Jiansheng Yang, Xiaohu Guo, Qiang Kong, Tie Zhou, Ming Jiang, "Parallel Implementation of theKatsevich's FBP Algorithm", International Journal of Biomedical Imaging, Special Issue on"Development of Computed Tomography Algorithms", 2006, Article ID 17463.(17)Ge Wang, Ming Jiang, Jie Tian,Wenxiang Cong, Yi Li, Weimin Han, Durai Kumar, Xin Qian, HaiouShen, Tie Zhou, Jiantao Cheng, Y ujie Lv, Hui Li, Jie Luo, "Recent development in bioluminescence tomography", 2006 3RD IEEE INTERNA TIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM MACRO TO NANO, VOLS 1-3, IEEE International Symposium on Biomedical Imaging, pages: 678-681, 2006. (SCI)(18)Ming Jiang, Tie Zhou, Jiantao Cheng, Wengxiang Cong, Durairaj Kumar, Ge Wang, "ImageReconstruction for Bioluminescence Tomography", RSNA 2005.(19)Ming Jiang, Tie Zhou, Jiantao Cheng, Wenxiang Cong, Ge Wang, "Development of bioluminescencetomography", art. no. 63180E, Developments in X-Ray Tomography V, PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE), vol. 6318, pages: E3180-E3180, 2006. (SCI)(20)Jinxiao Pan, Tie Zhou, Yan Han, Ming Jiang, “Variable Weighted Ordered Subset ImageReconstruction Algorithm”, International Journal of Biomedical Imaging, V olume 2006 (2006), doi:10.1155/IJBI/2006/10398 Article ID 10398, 7 pages(21)Seung Wook Lee, Jinxiao Pan, Chunhua Liu, Tie Zhou, Cheul-Muu Sim, Ming Jiang, A PreliminaryStudy of Iterative Reconstruction Algorithms for Neutron Tomography, The 8th World Conference on Neutron Radiography, National Institute of Standards and Technology, Gaithersburg, MD, 16 - 19 October, 2006. (SCI)(22)G. Wang, X. Qian, W. Cong, H. Shen, Y. Li, W. Han, D. Kumar, M. Jiang, T. Zhou, J. Cheng, J. Tian,Y. Lv, Hui Li, J. Luo, "Recent development in bioluminescence tomography", Current MedicalImaging Reviews, V olume 2, Number 4, November 2006. (SCI)(23)Tony Chan, Haomin Zhou, Tie Zhou, "Error Analysis for H^1 Based Wavelet Interpolations", ImageProcessing Based on Partial Differential Equations: Proceedings of the International Conference on PDE-Based Image Processing and Related Inverse Problems, Editors: X.-C. Tai, K.-A. Lie, T.F.Chan, and S. Osher, Series: Mathematics and Visualization, Springer Verlag, 2007.(24)Jiang, Ming; Louis, Alfred K.; Wolf, Didier; Zhao, Hongkai; Daul, Christian; Zhang, Zhaotian;Zhou, Tie, Mathematics in biomedical imaging: Editorial, International Journal of BiomedicalImaging, v 2007, Article number: 64954. 2007. (EI)(25)M. Jiang, T. Zhou, J. Cheng, W. Cong, G. Wang, "Image reconstruction for bioluminescencetomography from partial measurement", OPTICS EXPRESS, V ol. 15, No. 18, September 2007.(SCI)(26)Wenlei Ni, Ming Jiang, Shulin Zhou, Tie Zhou, “On Reconstruction Algorithms of X-ray PhaseContrast CT by Holographic Measurements” (invited), An Interdisciplinary Workshop onMathematical Methods in Biomedical Imaging and Intensity-Modulated Radiation Therapy (IMRT), Centro di Ricerca Matematica Ennio De Giorgi, Scuola Normale Superiore di Pisa, Italy, October 15 - 20, 2007.(27)Ni Wen-lei, Zhou Tie, "Algorithm for phase contrast X-ray tomography based on nonlinear phaseretrieval", Applied Mathematics and Mechanics (English Edition), Vol.29, No.1, pp101-112, 2008.(SCI)(28)Jiantao Cheng, Tie Zhou, "A Variational EM Method for the Inverse Black Body RadiationProblem", Journal of Computational Mathematics (English Edition), V ol.26, No.6, pp876–890, 2008.(SCI)(29)Tie Zhou, Jiantao Cheng, Ming Jiang, Bioluminescence Tomography Reconstruction by Radial BasisFunction Collocation Method, Industrial and Applied mathematics in China (Series in Contemporary Applied Mathematics CAM 10), pp229-239, Higher Education Press, 2009.(30)Caifang Wang, Tie Zhou, “Local convergence of an EM-like image reconstruction method fordiffuse optical tomography”, Journal of Computational Mathematics (English Edition), Vol.29, No.1, pp.61-73, 2011. (SCI)(31)Caifang Wang, Tie Zhou, “The order of convergence for Landweber Scheme with α,β –rule”, InverseProblems and Imaging,V ol.6, No.1, pp.133-146, 2012. (SCI)(32)周宇，周铁，姜明，“X射线相位相位衬度层析成像的数学模型”，数学建模及其应用，第1卷，第2期，pp.12-18，2012.(33)Yu Zhou, Tie Zhou, Ming Jiang, “An alternative derivation for Bronnikov’s formula in x-ray phasecontrast tomography”, World Congress on Medical Physics and Biomedical Engineering, ofInternational Federation for Medical and Biological Engineering (IFMBE) Proceedings, vol.39,pp.1038-1040, 2012.(EI)(34)Yanbin Lu, Jiansheng Yang, John W Emerson, Heng Mao, Tie Zhou,Y uanzheng Si, Ming Jiang,“Cone-beam reconstruction for the two-circles-plus-one-line trajectory”, Physics in Medicine and Biology”, Vol.57, No.9, pp.2689-2707, 2012.(SCI)(35)S. Luo, H. Zhou, T. Zhou, “An Improved Color Image Demosaicking Algorithm”, 2012 5thInternational Congress on Image and Signal Processing (CISP 2012), 2012-10. (EI)(36)Yu Zhou, Alfred K Louis, Tie Zhou, Ming Jiang, “Partial Coherence Theory for X-ray PhaseContrast Imaging Technique with Gratings”, Optics Communications，V olume 285, Issue 24, Pages 4763–4774, 2012-11. (SCI)(37)Seung Wook Lee, Yu Zhou, Tie Zhou, Ming Jiang, Jongyul Kim, Chiwon Ahn, Alfred K. Louis,Visibility studies in grating-based neutron phase contrast and dark-field imaging by partial coherence theory, Journal of Korean Physical Society, V ol. 63, NO. 11, pp. 2093 - 2097, 2013.(38)Shousheng Luo, Jiansheng Yang and Tie Zhou, “Moment-based cosh-Hilbert Inversion and ItsApplications in Single-photon Emission Computed Tomography”, CHINESE JOURNAL OFCOMPUTATIONAL PHYSICS（计算物理）, Vol. 30, No. 6, pp.799-807, Nov. 2013.（核心期刊）(39)Shousheng Luo, Tie Zhou, “Superiorization of EM Algorithm and Its Application in Single-PhotonEmission Computed Tomography(SPECT) ”, Inverse Problems and Imaging , Vol 8, Issue 1, Pages: 223 - 246, February 2014.(SCI)(40)Tangjie Lv, Tie Zhou, “V ARIATIONAL ITERA TIVE ALGORITHMS IN PHOTOACOUSTICTOMOGRAPHY W ITH V ARIABLE SOUND SPEED”, Journal of Computational Mathematics, Vol.32, No.5, pp.579-600, Aug. 2014. (SCI)(41)毛珩，李宣成，李海文，TAO Louis，周铁，“基于主动轮廓模型的运动线虫中心线定位算法”，中国科学：数学46(7)，pp.1005-1016，2016.(42)Ji Li and Tie Zhou, “On gradient descent algorithm for generalized phase retrieval problem”,Proceedings of IEEE Conference on Signal Processing (ICSP 2016).(EI)(43)Ji Li and Tie Zhou, “On Relaxed Averaged Alternating Reflections (RAAR) Algorithm for PhaseRetrieval from Structured I lluminations”, Inverse Problems, 33 (2), 025012(20pp), 2017. (SCI) (44)Ji Li and Tie Zhou, “NUMERICAL OPTIMIZATION ALGORITHMS FOR WA VEFRONT PHASERETRIEV AL FROM MULTIPLE MEASUREMENTS”, Inverse Problems and Imaging 11(4),pp.721-743, 2017.(SCI)(45)Ji Li, Tie Zhou and Chao Wang, “On global convergence of gradient descent algorithms forgeneralized phase retrieval problem”, Journal of Computational and Applied Mathematics, Volume 329, Pages 202-222, 2017.(SCI)(46)Chao Wang, Tie Zhou, “On Iterative Algorithms for Quantitative Photoacoustic Tomography in theRadia tive Transport Regime”, Inverse Problems, 33(11), 115006 (25pp) , 2017. (SCI)(47)Chao Wang, Tie Zhou, “A Hybrid Reconstruction Approach for Absorption Coefficient byFluorescence Photoacoustic Tomography”, Inverse Problems, 35 (2019) 025005, 2019. (SCI) (48)S. Luo, Y. Zhang, T. Zhou and J. Song , “XCT Image Reconstruction by a Modified SuperiorizedIteration and Theoretical Analysis”, Optimization Methods and Software, online,https:///10.1080/10556788.2018.1560442, 2019. (SCI)。
图像加密英文翻译 译文
编号：毕业设计(论文)英文翻译（译文）学院：数学与计算科学学院专业：信息与计算科学学生姓名：覃洁文学号： 1000710222指导教师单位：数学与计算科学学院姓名：王东职称：副教授2014年 6 月7 日Parallel image encryption algorithm based on discretized chaotic mapAbstractRecently, a variety of chaos-based algorithms were proposed for image encryption. Nevertheless, none of them works efficiently in parallel computing environment. In this paper, we propose a framework for parallel image encryption. Based on this framework, a new algorithm is designed using the discretized Kolmogorov flow map. It fulfills all the requirements for a parallel image encryption algorithm. Moreover, it is secure and fast. These properties make it a good choice for image encryption on parallel computing platforms.1. IntroductionIn recent years, there is a rapid growth in the transmission of digital images through computer networks especiallythe Internet. In most cases, the transmission channels are not secure enough to prevent illegal access by malicious listeners. Therefore the security and privacy of digital images have become a major concern. Many image encryption methods have been proposed, of which the chaos-based approach is a promising direction [1–9].In general, chaotic systems possess several properties which make them essential components in constructingcryptosystems:(1) Randomness: chaotic systems generate long-period, random-like chaotic sequence ina deterministic way.(2) Sensitivity: a tiny difference of the initial value or system parameters leads to a vast change of the chaoticsequences.(3) Simplicity: simple equations can generate complex chaotic sequences.(4) Ergodicity: a chaotic state variable goes through all states in its phase space, and usually those states are distributeduniformly.In addition to the above properties, some two-dimensional (2D) chaotic maps are inherent excellent alternatives forpermutation of image pixels. Pichler and Scharinger proposed a way to permute the image using Kolmogorov flow mapbefore a diffusion operation [1,2]. Later, Fridrich extended this method to a more generalized way [3]. Chen et al. proposedan image encryption scheme based on 3D cat maps [4]. Lian et al. proposed another algorithm based on standardmap [5]. Actually, those algorithms work under the same framework: all the pixels are first permuted with a discretizedchaotic map before they are encrypted one by one under the cipher block chain (CBC) mode where the cipher of the current pixel is influenced by the cipher of previous pixels. The above processes repeat for several rounds and finally thecipher-image is obtained.This framework is very effective in achieving diffusion throughout the whole image. However, it is not suitable forrunning in a parallel computing environment. This is because the processing of the current pixel cannot start until theprevious one has been encrypted. The computation is still in a sequential mode even if there is more than one processingelement (PE). This limitation restricts its application platform since many devices based on FPGA/CPLD or digital circuitscan support parallel processing. With the parallel computing technique, the speed of encryption is greatly accelerated.Another shortcoming of chaos-based image encryption schemes is the relatively slowcomputing speed. The primaryreason is that chaos-based ciphers usually need a large amount of real number multiplication and division operations,which cost vast of computation. The computational efficiency will be increase substantially if the encryption algorithmscan be executed on a parallel processing platform.In this paper, we propose a framework for parallel image encryption. Under such framework, we design a secure andfast algorithm that fulfills all the requirements for parallel image encryption. The rest of the paper is arranged as follows. Section 2 introduces the parallel operating mode and its requirements. Section 3 presents the definitions and propertiesof four transformations which form the encryption/decryption algorithm. In Section 4, the processes ofencryption, decryption and key scheduling will be described in detail. Experimental results and theoretical analysesare provided in Sections 5 and 6, respectively. Finally, we conclude this paper with a summary.2. Parallel mode2.1 Parallel mode and its requirementsIn parallel computing mode, each PE is responsible for a subset of the image data and possesses its own memory.During the encryption, there may be some communication between PEs (see Fig. 1).To allow parallel image encryption, the conventional CBC-like mode must be eliminated. However, this will cause anew problem, i.e. how to fulfill the diffusion requirement without such mode. Besides, there arise some additional requirements for parallel image encryption:1. Computation load balance The total time of a parallel image encryption scheme is determined by the slowest PE, since other PEs have to waituntil such PE finishes its work. Therefore a good parallel computation mode can balance the task distributed to each PE.2. Communication load balance There usually exists lots of communication between PEs. For the same reason as of computation load, the communication load should be carefully balanced.3. Critical area management When computing in a parallel mode, many PEs may read or write the same area of memory (i.e. critical area) simultaneously,which often causes unexpected execution of the program. It is thus necessary to use some parallel techniquesto manage the critical area.2.2 A parallel image encryption frameworkTo fulfill the above requirements, we propose a parallel image encryption framework, which is a four-step process: Step 1: The whole image is divided into a number of blocks. Step 2: Each PE is responsible for a certain number of blocks. The pixels inside a block are encrypted adequately witheffective confusion and diffusion operations. Step 3: Cipher-data are exchanged via communication between PEs to enlarge the diffusion from a block to a broaderscope. Step 4: Go to step 2 until the cipher image reaches the required level of security.In step 2, diffusion is achieved, but only within the small scope of one block. With theaid of step 3, however, suchdiffusion effect is broadened. Note that from the cryptographic point of view, data exchange in step 3 is essentially apermutation. After several iterations of steps 2 and 3, the diffusion effect is spread to the whole image. This means thata tiny change in one plain-image pixel will spread to a substantial amount of pixels in the cipher-image. To make theframework sufficiently secure, two requirements must be fulfilled:1. The encryption algorithm in step 2 should be sufficiently secure with the characteristic of confusion and diffusion aswell as sensitivity to both plaintext and key.2. The permutation in step 3 must spread the local change to the whole image in a few rounds of operations.The first requirement can be fulfilled by a combination of different cryptographic elements such as S-box, Feistel-structure,matrix multiplications and chaos map, etc., or we can just use a conventional cryptographic standard suchas AES or IDEA. The second one, however, is a new topic resulted from this framework. Furthermore, such permutationshould help to achieve the three additional goals presented in Section 2.1. Hence, the permutation operation isone of the focuses of this paper and should be carefully studied.Under this parallel image encryption framework, we propose a new algorithm which is based on four basic transformations. Therefore, we will first introduce those transformations before describing our algorithm.3. Transformations3.1 A-transformationIn A-tran sformation, …A‟ stands for addition. It can be formally defined as follow: a+b=c ,where a,b,cϵG,G=GF(28), and the addition is defined as the bitwise XOR operation. The transformation A has three fundamental properties:(2.1)a+a=0(2.2)a+b=b+a (2)(2.3)(a+b)+c=a+(b+c)3.2 M-transformationIn M-transformation, …M‟ stands for mixing of data. First, we introduce the sum transformation: sum：m×n→Gthensum(I) is defined as: sum（1）= a（ij）Now we give the definition of M-transformation as follows: M：m×n→m×nLet M（I）=C I= a（ij）C=（c（ij）(3) c（ij）=a（ij）+sum（I）It is easy to prove the following properties of the M-transformation:（5.1）M（M（I））=I（5）(4) （5.2）M（I+J）=M（I）+M（J）（5.3）M（kj）=kM（I），where kI=1，k∈NIt should be noted that all the addition operations from are the A-transformation indeed.3.3 S-transformationIn S-transformation, …S‟ stands for S-box substitution. There are lots of ways to constructan S-box, among whichthe chaotic approach is a good candidate. For example, Tang et al presented a method to design S-box based on discretized logistic map and Baker map [10]. Following this work, Chen et al. proposed another method to obtain an S-box,which leads to a better performance [11]. The process is described as follows:Step 1: Select an initial value for the Chebyshev map. Then iterate the map to generate the initial S-box table.Step2: Pile up the 2D table to a 3D one.Step 3: Use the discretized 3D Baker map to shuffle the table for many times. Finally, transform the 3D table back to2D to obtain the desired S-box. Experimental results show that the resultant S-box is ideal for cryptographic applications. The approach is alsocalled …dynamic‟ as different S-boxes are obtained when the initial value of Chebyshev map is changed. However,for the sake of simplicity and performance, we use a fixed S-box, i.e. the example given in [11] (see Table 1).3.4 K-transformationIn K-transformation, …K‟ stands for Kolmogorov flow, which is often called generalized Baker map [3]. The applicationof Kolmogorov flow for image encryption was first proposed by Pichler and Scharinger [1,2]. The discrete version of K-flow is given by :whered = (n1,n2, . . . ,nk), ns is an positive integer, and ns divide N for all s, = 1/ns, while Fsis still the leftbound of the vertical strip s:Note that the Eq. (6) can be interpreted by the geometrical transformation shown in Fig.2. The N ·N image is firstdivided into vertical rectangles of height N and width ns. Then each vertical rectangle is further divided into boxes ofheight psand width ns. After K-transformation, pixels from the same box are actually mapped to a single row.Table 1The proposed S-box is the example given in [11]161 85 129 224 176 50 207 177 48 205 68 60 1 160 117 46130 124 203 58 145 14 115 189 235 142 4 43 13 51 52 19152 153 83 96 86 133 228 136 175 23 109 252 236 49 167 92106 94 81 139 151 134 245 72 172 171 62 79 77 231 82 32238 22 63 99 80 217 164 178 0 154 240 188 150 157 215 232180 119 166 18 141 20 17 97 254 181 184 47 146 233 113 12054 21 183 118 15 114 36 253 197 2 9 165 132 204 226 64107 88 55 8 221 65 185 234 162 210 250 179 61 202 248 247213 89 101 108 102 45 56 5 212 10 12 243 216 242 84 111143 67 93 123 11 137 249 170 27 223 186 95 169 116 163 25174 135 91 104 196 208 148 24 251 39 40 31 16 219 214 74140 211 112 75 190 73 187 244 182 122 193 131 194 149 121 76156 168 222 34 241 70 255 229 246 90 53 225 100 30 37 237103 126 38 200 44 209 42 29 41 218 71 155 78 125 173 28128 87 239 3 191 158 199 138 227 59 69 220 195 66 192 2304 MASK–aparallel image encryption scheme4.1.Outline of the proposed encryption schemeAssume the N·Nimage is encrypted by nPEssimultaneously, we describe the parallel encryption schemeas follows:1.Each PE is responsible for some fixe drow so fpixelsin the image.2. PixelsofeachrowareencryptedusingtransformationM,A,S,respectively.3. Permuteall the pixelsaccording to transformation Ktohavefur the rdiffusion.4. Goto step 2 forano the rroun do fencryptio nuntilthecip her issufficiently secure.Thereforeboththepermutationmapanditsparametersmustbecarefullychosen.Inouralgorithm ,disaconstantvectorwithlengthq,whereq=N/n.Eachelementofthevectorisequalton Each PE is responsible for q consecutive rows, or more specifically, the ith PE is responsible for rows from (i-1) * qto i* q-1. This algorithm can fulfil all the requirements for parallel encryption, as analyzed below.1.Diffusion effect in the whole imageAssume that the operations in step 2 are sufficiently secure. After step 2, a tiny change of the plain pixel will diffuse tothe whole row of N pixels. If we choose d according to Eq. (7), it is easy to prove that those N cipher pixels will bepermuted to different q rows with the help of K-transformation in step 3. In the same way, after another round ofencryption, the change is spread to q rows, and after the third round, the whole cipher image is changed. Consequently,in our scheme, the smallest change of any single pixel will diffuse to the whole image in 3 rounds.2.Balance of communication loadIf the parameter d of (6) is chosen as (7), it is easy to prove that the data exchanged between two PEs are constant,i.e., equal to 1/q2 of the total number of image pixels. For each PE, this quantity becomes (q _ 1)/q2. Therefore, inour scheme, the communication load of each PE is equivalent, and there is no unbalance of communication load forthe PEs at all.3. Balance of computation loadThe data to be encrypted by each PE is equally q rows of pixels; hence computation load balance is achievednaturally.4. Critical area managementIn our scheme, under no circumstances would two PEs read from or write to the same memory. Therefore, we do notneed to impose any critical area management technique in our scheme as other parallel computation schemes oftendo.The above discussions have shown that the proposed scheme fullfil all the requirements for parallel image encryption ,which is mainly attributed to the chaotic Kolmogorov map and the choice of its parameters.4.2 CipherThe cipher is made up of a number of rounds. However, before the first round, the image is pre-processed with a K-transformation.Then in each round, the transformation M, A, S, K is carried out, respectively. The final round differsslightly from the previous rounds in that the S-transformation is omitted on purpose. The transformations M, A, Soperate on one row of pixels by each PE, while the transformation K operates on the whole image which necessarilyinvolves communicationbetween PEs. The cipher is described by the pseudo-code listed in Fig. 3.4.3 Round key generationAmong the four transformations, only transformation A needs a round key. For an 8-bit grey level image of N ·Npixels, a round key containing N bytes should be generated for transformation A in each round.Generally speaking, the round keys should be pseudo-random and key-sensitive. From this point of view, a chaotic map is a good alternative. In our scheme, we use the skew tent map to generate the required round keys.x/μ,0<x<μx(1)=(1−x)/(1−u) ,μ<x<1 (8) The chaotic sequence is determined by the system parameter l and initial state x0 of the chaotic map either of whichis a real number between 0 and 1. Although the chaotic map equation is simple, it generates pseudo-random sequencesthat are sensitive to both the system parameter and the initial state. This property makes the map an ideal choice for keygeneration.When implemented in a digital computer, the state of the map is stored as a floating point number. The first 8 bits ofeach state are extracted as one byte of the round key. Accordingly, we need to iterate the skew tent map for N times ineach round.4.4 DecipherIn general, the decryption procedures are composed of a reversed order of the transformations performed in encryption. This property also holds in our scheme. However, with careful design, the decryption process of our scheme canhave the same, rather than the reversed, order of transformations as the cipher. This impressive characteristic attributesto two properties of the transformations:(1)Transformation-S and transformation-K are commutable. Transformation-S substitutes only the value of eachpixel and is independent of its position. On the other hand, transformation-K changes only a pixel‟s position with It‟s value unchanged. Consequently, the relation between the twotransformations can be expressed in (9):K(S(I))=S(K(I))(2) Transformation-Mis a linear operation according to (5). Moreover, the addition defined in (5.2) is actually transformationA. Thus the relation between the two transformations can be expressed in (10):M(A(I,J))=A(M(I),M(J)In short, either transformations S and K, or transformations M and Acan interchange their computation order withno influence on the final result. Table 2 illustrates how these two properties affect the order of the transformations ofdecipher in a simple example of 2-round cipher.It is easy to observe that for a cipher composed of multiple rounds, the decipher process still has the same sequenceof transformations as the cipher. Hence, both the cipher and decipher share the same framework. However, there are still some slight differences between the encryption and decryption processes:(1)The round keys used in decipher is in a reversed order of that in cipher, and those keysshould be first applied thetransformation M.(2)The transformation K and S in decipher should use their inversetransformations.However, since transformation K and S can both be implemented by look-up table operations, their inverse transformationsdiffer just in content of look-up tables. Consequently, all above difference in computations can be translatedinto difference in data.The symmetric property makes our scheme very concise. It also reduces lots of codes for a computer system implementingboth the cipher and decipher. For hardware implementation, this property results in a reduction of cost forboth devices.Table 2 The process of equivalent decipher in 2-round encryptionThe remarkable structure of our scheme looks more concise and saves a lot of codes during implementation. This isdefinitely an advantage when compared with other chaos-based ciphers5 Experimental resultsIn this section, an example is given to illustrate the effectiveness of the proposed algorithm. In the experiment, a greylevel image …Lena‟ of size 256 * 256 pixels, as shown in Fig. 4a, is chosen as the plain-image. The number of PEs is chosenas 4. The key of our system, i.e. the initial state x0 and the system parameter l are stored as floating point numberwith a precision of 56-bits. In the example presented here, x0 = 0.12345678, and l = 1.9999.When the encryption process is completed, the cipher-image is obtained and is shown in Fig. 4b. Typically, weencrypt the plain-image for 9 rounds as recommended.5.1 HistogramHistogram of the plain-image and the cipher-image is depicted in Figs. 4c and d, respectively. These two figures showthat the cipher-image possesses the characteristic of uniform distribution in contrast to that of the plain-image.5.2 Correlation analysis of two adjacent pixelsThe correlation analysis is performed by randomly select 1000 pairs of two adjacent pixels in vertical, horizontal,and diagonal direction, respectively, from the plain-image and the ciphered image. Then the correlation coefficientof the pixel pair is calculated and the result is listed in Table 3. Fig. 5 shows the correlation of two horizontally adjacentpixels. It is evident that neighboring pixels of the cipher-image has little correlation.5.3 NPCR analysisNPCR means the change rate of the number of pixels of the cipher-image when only one pixel of the plain-image ismodified. In our example, the pixel selected is the last pixel of the plain-image. Its value is changed from (01101111)2 to (01101110)2. Then the NPCR at different rounds are calculated and listed in Table 4. The data show that the performanceis satisfactory after 3 rounds of encryption. The different pixels of the two cipher-images after 9 rounds are plottedin Fig. 6. Fig. 4.(a) Plain-image, (b) cipher-image, (c) histogram of plain-image, (d) histogram of cipher-image. Table 3 Correlation coefficient of two adjacent pixels in plain-image and cipher-image. Fig.5. Correlation of two horizontally adjacent pixels of (a) plain image; (b) cipher image, x-coordinate and y-coordinate is the greylevel of twoneighbor pixels, respectively. Table 4 NPCR of two cipher-images at different rounds.5.4 UACI analysisThe unified average changing intensity (UACI) index measures the average intensity of differences between twoimages. Again, we make the same change as in Section 5.3 and calculate the UACI between two cipher-images. Theresults are in Table 5. After three rounds of encryption, the UACI is converged to 1/3. It should be noticed thatthe average error between two random sequences uniformly distribution in [0, 1] is 1/3 if they are completely uncorrelatedwith each other. Fig. 6.Difference between two ciphered images after 9 rounds. White points (about 1/256 of the total pixels) indicate the positionswhere pixels of the two cipher-images have the same values. Table 5 UACI of two cipher-images.6 Security and performance analysis6.1 DiffusionThe NPCR analysis has revealed that when there is only 1 bit of the plain pixel changed, almost all the cipher pixelsbecome different although there is still a low possibility of 1/256 that two cipher pixels are equal. This diffusion firstattributes to transformation S since change of any bit of the input to the S-box influences all the output bits at a changerate of 50%. Then, the diffusion is spread out to the whole row by transformation M. Finally, transformation K helps toenlarge the diffusion to 25% of the image in 2 rounds, and to the whole image in 3 rounds.6.2 ConfusionThe histogram and correlation analyses of adjacent pixels both indicate that our scheme possesses a good propertyof confusion. This mainly results from the pseudo-randomness of the key schedule and transformations M and A. Theywork together to introduce the random-like effect to the cipher image. Transformation K is also helpful in destroyingthe local similarity of the plain-image.6.3 Brute-force attackThe proposed scheme uses both the initial state x0 and the system parameter l as the secret key whose total number of bits is 112. It is by far very safe for ordinary business applications. Therefore, our scheme is strong enough to resistbrute-force attack. Moreover, it is very easy to increase the number of bits for both x0 and μ.6.4 Other security issuesSomeone may argue that, in our scheme, transformation K is not governed by any key. However, this reduces littlesecurity of our scheme. As a matter of fact, most conventional encryption algorithms such as DES and AES use publicpermutations. There are at least two reasons for it. First, permutations governed by key slow down the speed of encryption,for it costs time to generate those permutations from the key. Secondly and the foremost, weak permutations maybe generated from some keys, which harm to the security of the system. Actually, a permutation that helps to achievediffusion and confusion is a better alternative.More specifically, in a parallel encryption system, if the permutationhelps to achieve computation and communication load balance, it is a good alternative. From this point of view, transformationK is a proper choice.6.5 Performance analysisThe proposed algorithm runs very fast as there are only logical XOR and table lookup operations in the encryptionand decryption processes. Although multiplications and divisions are required in transformation K, the transformationis fixed once the number of PE is fixed. Hence, they can be pre-computed and stored in a lookup table. More accurately,there are only 3 XOR operations (2 for transformation M and 1 for transformation A) and 2 lookup table operations (1 for transformation S and 1 for transformation K) for each pixel in each round. On the contrary, for the simplest logisticmap with 56-bit precision state variable, one multiplication costs about 28 additions in average. In our keyschedule,multiplications are also required in the skew tent map. However, there are only N such multiplications in each round ,and hence an average of 1/N multiplications for each pixel per round. Furthermore, when the algorithm runs on a parallel platform, the performance can increase nearly n times than ordinary sequential image encryption scheme. Therefore, as far as the performance is concerned, our scheme is superior than existing ones.7 ConclusionIn this paper, we introduced the concept of parallel image encryption and presented several requirements for it. Thena framework for parallel image encryption was proposed and a new algorithm was designed based on this framework. The proposed algorithm is successful in accomplishing all the requirements for a parallel image encryption algorithmwith the help of discretized Kolmogorov flow map. Moreover, both the experimental results and theoretical analysesshow that the algorithm possesses high security. The proposed algorithm is also fast; there are only a couple ofXOR operations and table lookup operations for each pixel. Finally, the decryption process is identical to that ofthe cipher. Taking into account all the virtues mentioned above, the proposed algorithm is a good choice for encryptingimages in a parallel computing platform.References[1] Pichler F, Scharinger J. Ciphering by Bernoulli shifts in finite Abelian groups. Contributions togeneralalgebra. Proc. Linzconference1994. p. 465–76.[2] Scharinger J. Fast encryption of image data using chaotic Kolmogorov flows. J Electron Image1998;7(2):318–25.[3] Fridrich J. Symmetric ciphers based on two-dimensional chaotic maps. I J Bifur Chaos1998;8(6):1259–64.[4] Chen G, Mao Y, Chui C. Symmetric image encryption scheme based on 3D chaotic cat maps. Chaos,Solitons& Fractals2004;21(3):749–61.[5] Lian S, Shun J, Wang Z. A block cipher based on a suitable use of the chaotic standard map. Chaos,Solitons& Fractals2005;26(1):117–29.[6] Guan Z, Huang F, Guan W. Chaos-based image encryption algorithm. PhysLett A 2005;346(1–3):153–7.[7] Zhang L, Liao X, Wang X. An image encryption approach based on chaotic maps. Chaos, Solitons&Fractals 2005;24(3):759–65.[8] Gao H, Zhang Y, Liang S, Li D. A new chaotic algorithm for image encryption. Chaos, Solitons&Fractals 2006;29(2):393–9.[9] Pareek NK, Patidar V, Sud KK. Image encryption using chaotic logistic map. Image Vision Comput2006;24(9):926–34.[10] Tang Guoping, Liao Xiaofeng, Chen Yong. A novel method for designing S-boxes based on chaoticmaps. Chaos, Solitons&Fractals 2005;23:413–9.[11] Chen G, Chen Y, Liao X. An extended method for obtaining S-boxes based on three-dimensionalchaotic Baker maps. Chaos,Solitons& Fractals 2007;31(3):571–9Digital Image Processing1 IntroductionMany operators have been proposed for presenting a connected component n a digital image by a reduced amount of data or simplied shape. In general we have to state that the development, choice and modi_cation of such algorithms in practical applications are domain and task dependent, and there is no \best method". However, it is interesting to note that there are several equivalences between published methods and notions, and characterizing such equivalences or di_erences should be useful to categorize the broad diversity of published methods for skeletonization. Discussing equivalences is a main intention of this report.1.1 Categories of MethodsOne class of shape reduction operators is based on distance transforms. A distance skeleton is a subset of points of a given component such that every point of this subset represents the center of a maximal disc (labeled with the radius of this disc) contained in the given component. As an example in this _rst class of operators, this report discusses one method for calculating a distance skeleton using the d4 distance function which is appropriate to digitized pictures. A second class of operators produces median or center lines of the digital object in a non-iterative way. Normally such operators locate critical points _rst, and calculate a speci_ed path through the object by connecting these points.The third class of operators is characterized by iterative thinning. Historically, Listing [10] used already in 1862 the term linear skeleton for the result of a continuous deformation of the frontier of a connected subset of a Euclidean space without changing the connectivity of the original set, until only a set of lines and points remains. Many algorithms in image analysis are based on this general concept of thinning. The goal is a calculation of characteristic properties of digital objects which are not related to size or quantity. Methods should be independent from the position of a set in the plane or space, grid resolution (for digitizing this set) or the shape complexity of the given set. In the literature the term \thinning" is not used .in a unique interpretation besides that it always denotes a connectivity preserving reduction operation applied to digital images, involving iterations of transformations of speci_ed contour points into background points. A subset Q _ I of object points is reduced by a de_ned set D in one iteration, and the result Q0 = Q n D becomes Q for the next iteration.。
采用DSP+FPGA为核心实现DSP的高速并行处理系统
表３数据处理板ＤＳＰ单元的ＵｐＬｉｎｋ连接、分配关系
对外Ｌｉｎｋ口
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对应的ＤＳＰ Ｌｉｎｋ ＴＳ０＿ＬＩＮＫ２
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４ 吴小华，李白田，张 帆．干涉超光谱图像分析与近无损压缩 ＣＰＬＤ实现，光子学报，２００５；３４（９）：１３４６－－１３５０
Ｓｙｓｔｅｍ ｏｆ Ｈｉｇｈ－ｓｐｅｅｄ ＤＳＰ Ｐａｒａｌｌｅｌ Ｐｒｏｃｅｓｓｉｎｇ Ｂａｓｅｄ ｏｎ ＤＳＰ＋ＦＰＧＡ
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万方数据
采用DSP+FPGA为核心实现DSP的高速并行处理系统
image_processing_toolbox
Image Processing Toolbox 7.0Perform image processing, analysis, and algorithm developmentImage Processing Toolbox™ provides a comprehensive set of reference-standard algorithms and graphical tools forimage processing, analysis, visualization, and algorithm development. You can perform image enhancement,image deblurring, feature detection, noise reduction, image segmentation, spatial transformations, and imageregistration. Many functions in the toolbox are multithreaded to take advantage of multicore and multiprocessorcomputers.Image Processing Toolbox supports a diverse set of image types, including high dynamic range, gigapixelresolution, ICC-compliant color, and tomographic images. Graphical tools let you explore an image, examine aregion of pixels, adjust the contrast, create contours or histograms, and manipulate regions of interest (ROIs).With the toolbox algorithms you can restore degraded images, detect and measure features, analyze shapes andtextures, and adjust the color balance of images.Key Features▪Image enhancement, filtering, and deblurring▪Image analysis, including segmentation, morphology, feature extraction, and measurement▪Spatial transformations and image registration▪Image transforms, including FFT, DCT, Radon, and fan-beam projection▪Workflows for processing, displaying, and navigating arbitrarily large images▪Modular interactive tools, including ROI selections, histograms, and distance measurements▪ICC color management▪Multidimensional image processing▪Image-sequence and video display▪DICOM import and exportImporting and Exporting ImagesImage Processing Toolbox supports images generated by a wide range of devices, including digital cameras,satellite and airborne sensors, medical imaging devices, microscopes, telescopes, and other scientific instruments.You can visualize, analyze, and process these images in many data types, including single- and double-precisionfloating-point and signed and unsigned 8-, 16-, and 32-bit integers.There are several ways to import and export images into and out of the MATLAB® environment for processing.You can use Image Acquisition Toolbox™ to acquire live images from Web cameras, frame grabbers,DCAM-compatible cameras, and other devices. Using Database Toolbox™, you can access images stored inODBC/JDBC-compliant databases.MATLAB supports standard data and image formats, including JPEG, JPEG-2000, TIFF, PNG, HDF, HDF-EOS,FITS, Microsoft® Excel®, ASCII, and binary files. It also supports the multiband image formats BIP and BIL, asused by LANDSAT for example. Low-level I/O and memory mapping functions enable you to develop customroutines for working with any data format.Image Processing Toolbox supports a number of specialized image file formats. For medical images, it supports the DICOM file format, including associated metadata, as well as the Analyze 7.5 and Interfile formats. The toolbox can also read geospatial images in the NITF format and high dynamic range images in the HDR format.A display of MRI slices from a series of DICOM files, enabling visualization and exploration of medical images in MATLAB.Displaying and Exploring ImagesImage Processing Toolbox extends MATLAB graphics to provide image display capabilities that are highly customizable. You can create displays with multiple images in a single window, annotate displays with text and graphics, and create specialized displays such as histograms, profiles, and contour plots.In addition to display functions, the toolbox provides a suite of interactive tools for exploring images and building GUIs. You can view image information, zoom and pan around the image, and closely examine a region of pixels. You can interactively place and manipulate ROIs, including points, lines, rectangles, polygons, ellipses, and freehand shapes. You can also interactively crop, adjust the contrast, and measure distances. The suite of tools is available within Image Tool or from individual functions that can be used to create customized GUIs.A typical interactive session using Image Tool. The Overview window (left) is used to navigate when looking at magnified views in the Image Tool. The Pixel Region window (right) superimposes pixel values on a highly magnifiedview. LANDSAT image of Paris courtesy of Space Imaging, LLC.(bottom).The toolbox includes tools for displaying video and sequences in either a time-lapsed video viewer or an image montage. Volume visualization tools in MATLAB let you create isosurface displays of multidimensional image data sets.Video viewer paused on an individual frame of a video sequence.Preprocessing and Postprocessing ImagesImage Processing Toolbox provides reference-standard algorithms for preprocessing and postprocessing tasks that solve frequent system problems, such as interfering noise, low dynamic range, out-of-focus optics, and the difference in color representation between input and output devices.Image enhancement techniques in Image Processing Toolbox enable you to increase the signal-to-noise ratio and accentuate image features by modifying the colors or intensities of an image. You can:▪Perform histogram equalization▪Perform decorrelation stretching▪Remap the dynamic range▪Adjust the gamma value▪Perform linear, median, or adaptive filteringThe toolbox includes specialized filtering routines and a generalized multidimensional filtering function that handles integer image types, offers multiple boundary-padding options, and performs convolution and correlation. Predefined filters and functions for designing and implementing your own linear filters are also provided.Performing connected components analysis on an image with nonuniform background intensity using MATLAB and Image Processing Toolbox.Image deblurring algorithms in Image Processing Toolbox include blind, Lucy-Richardson, Wiener, and regularized filter deconvolution, as well as conversions between point spread and optical transfer functions. These functions help correct blurring caused by out-of-focus optics, movement by the camera or the subject during image capture, atmospheric conditions, short exposure time, and other factors. All deblurring functions work withmultidimensional images.Image of the sun using deblurring algorithms. Image courtesy of the SOHO EIT Consortium.Device-independent color management in Image Processing Toolbox enables you to accurately represent color independently from input and output devices. This is useful when analyzing the characteristics of a device, quantitatively measuring color accuracy, or developing algorithms for several different devices. With specialized functions in the toolbox, you can convert images between device-independent color spaces, such as sRGB, XYZ,xyY, L*a*b*, uvL, and L*ch.For more flexibility and control, the toolbox supports profile-based color space conversions using a color management system based on ICC version 4. For example, you can import n-dimensional ICC color profiles, create new or modify existing ICC color profiles for specific input and output devices, specify the rendering intent, and find all compliant profiles on your machine.Image transforms such as FFT and DCT play a critical role in many image processing tasks, including image enhancement, analysis, restoration, and compression. Image Processing Toolbox provides several image transforms, including Radon and fan-beam projections. You can reconstruct images from parallel-beam andfan-beam projection data (common in tomography applications). Image transforms are also available in MATLAB and Wavelet Toolbox™.Image conversions between data classes and image types are a common requirement for imaging applications. Image Processing Toolbox provides a variety of utilities for conversion between data classes, including single- and double-precision floating-point and signed or unsigned 8-, 16-, and 32-bit integers. The toolbox includes algorithms for conversion between image types, including binary, grayscale, indexed color, and truecolor. Specifically for color images, the toolbox supports a variety of color spaces (such as YIQ, HSV, and YCrCb) as well as Bayer pattern encoded and high dynamic range images.Analyzing ImagesImage Processing Toolbox provides a comprehensive suite of reference-standard algorithms and graphical tools for image analysis tasks such as statistical analysis, feature extraction, and property measurement.Statistical functions let you analyze the general characteristics of an image by:▪Computing the mean or standard deviation▪Determining the intensity values along a line segment▪Displaying an image histogram▪Plotting a profile of intensity valuesImage with histogram for the red channel.Edge-detection algorithms let you identify object boundaries in an image. These algorithms include the Sobel, Prewitt, Roberts, Canny, and Laplacian of Gaussian methods. The powerful Canny method can detect true weak edges without being "fooled" by noise.Image segmentation algorithms determine region boundaries in an image. You can explore many different approaches to image segmentation, including automatic thresholding, edge-based methods, andmorphology-based methods such as the watershed transform, often used to segment touching objects.Detection and outlining of an aircraft using segmentation and morphology.Morphological operators enable you to detect edges, enhance contrast, remove noise, segment an image into regions, thin regions, or perform skeletonization on regions. Morphological functions in Image Processing Toolbox include:▪Erosion and dilation▪Opening and closing▪Labeling of connected components▪Watershed segmentation▪Reconstruction▪Distance transformImage Processing Toolbox also contains advanced image analysis functions that let you:▪Measure the properties of a specified image region, such as the area, center of mass, and bounding box▪Detect lines and extract line segments from an image using the Hough transform▪Measure properties, such as surface roughness or color variation, using texture analysis functionsSpatial Transformations and Image RegistrationSpatial transformations modify the spatial relationships between pixels in an image and are useful for tasks such as rotating an image, creating thumbnails, correcting geometric distortions, and performing image registration. Image Processing Toolbox supports common transformational operations, such as resizing, rotating, and interactive cropping of images, as well as generalized transformations for arbitrary-dimensional arrays.Image registration is important in remote sensing, medical imaging, and other applications where images must be aligned to enable quantitative analysis or qualitative comparison. Using Image Processing Toolbox, you caninteractively select points in a pair of images and align the two images by performing a spatial transformation, such as linear conformal, affine, projective, polynomial, piecewise linear, or local weighted mean. You can also perform image registration using normalized 2D cross-correlation.Choosing control points to register an aerial photo to an orthophoto. The Control Point Selection Tool helps you select landmark points and align images (result, bottom). Color aerial photo (top left) courtesy of mPower3/Emerge. Grayscale orthophoto (top right) courtesy of MassGIS.Working with Large ImagesSome images are so large that they are difficult to process and display with standard methods. Image Processing Toolbox provides specific workflows for working with larger images than otherwise possible. Without loading a large image entirely into memory, you can create a reduced-resolution data set (R-Set) that divides an image into spatial tiles and resamples the image at different resolution levels. This workflow improves performance in image display and navigation. You can use a block processing workflow to apply a function to each distinct block of a large image, which significantly reduces memory use. An additional option for working with large images is to use the Parallel Computing Toolbox™.Product Details, Demos, and System Requirements/products/imageTrial Software/trialrequestSales/contactsalesTechnical Support/support A large multispectral image of Paris. Dividing the image into blocks for processing reduces memory usage. Image courtesy Space Imaging, LLC.ResourcesOnline User Community /matlabcentral Training Services /training Third-Party Products and Services /connections Worldwide Contacts /contact© 2010 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See /trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.。
IMAGE PROCESSING UNIT, ITS METHOD AND MEMORY READA
专利名称：IMAGE PROCESSING UNIT, ITS METHODAND MEMORY READABLE BY COMPUTER发明人：YAMAGATA SHIGEO,山形 茂雄,KABURAGIHIROSHI,蕪木 浩申请号：JP特願平8-192579申请日：19960722公开号：JP特開平10-42150A公开日：19980213专利内容由知识产权出版社提供专利附图：摘要：PROBLEM TO BE SOLVED: To use a plurality of filters efficiently by using the filters provided for filter processing of a color image also for a black/white image.SOLUTION: The unit is provided with a spatial filter 103 conducting parallel processing for component data of image data consisting of a plurality of component data, and an original image color discrimination section 111 selects whether the image data received from a color image input section 101 are to be processed as a monochromatic image or as a color image. As the result of selection, when the received image data are selected to be processed as a monochromatic image, the processing by the spatial filter 103 is controlled to apply stepwise to the image data.申请人：CANON INC,キヤノン株式会社地址：東京都大田区下丸子3丁目30番2号国籍：JP代理人：大塚 康徳 (外1名)更多信息请下载全文后查看。
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Parallel Numerics’05,151-163M.Vajterˇs ic,R.Trobec,P.Zinterhof,A.Uhl(Eds.) Chapter6:Multimedia ISBN961-6303-67-8Parallel Image Processing onConfigurable ComputingArchitectureAndriy Lutsyk1,∗,Oleksiy Lutsyk2,Olexandr Pelenskyy31Institute of Physics and Mechanics of the Ukrainian National Academy of Sciences,Naukova5,79601Lviv,Ukraine2Ivan Franko National University of Lviv,Kyrylo and Methodyi8,79005Lviv,Ukraine,3Westukrainian College,Chuprynka130,Lviv,Ukraine The recent advances in imaging technology make popular using imagesin different branches of human activity.In the paper,a reconfigurablecomputing architecture based on the homogeneous computing structureconcept with possibility of hardware modification in response to changesin processing environment and tuning on implementation of imagefilter-ing and segmentation algorithms in real time is presented.1IntroductionThe recent advances in imaging technology make popular using images in different branches of human activity.Robotics,biomedical applications,in-dustrial process control and environmental control are among them.Each procedure in this environment demands variety of processes,methods and hardware.To receive satisfactory results it is desirable to have both sophis-ticated methods and algorithms for image processing and compression which require considerable computational cost,and fulfilment this task in real time or near to real time.Nowadays various architectures are suggested for highly efficient image processing,including parallel processors of the SIMD type,multiprocessor systems,pipelined processors,systolic arrays and pyramid machines.In the ∗Corresponding author.E-mail:lutsyk@ah.ipm.lviv.ua152 A.Lutsyk,O.Lutsyk,O.Pelenskyy last years configurable computing devices are developed which may be able to adapt their hardware almost continuously in response to changes in the input data or processing environment[1,2].The configurable computing device is based onfield-programmable gate arrays(FPGAs)-tuned hardware circuits that can be modified during use.This article is organised so thatfirst the brief description of configurable architecture based on homogeneous computing structure is presented in Sec.2.In Section3,a microprogram module for addressfinding of the maximal or minimal number from two numbers is described in detail.Section4is ded-icated to the implementation of trimmed meanfiltering on the homogeneous computing structure.Mapping of a structure-adaptive imagefilter on the ho-mogeneous structure is described in Sec.5.Concluding remarks are given in Sec.6.2Configurable architecture based on homogeneous computing structureThe concept of the homogeneous computing structures was originated in the Former Soviet Union[3].The homogeneous computing structure(HCS)can be modified according to needs of data execution and can be used for speeding up of the majority of image processing and visual data compression algorithms which are applied in control,biomedicine and multimedia[4,5].In this paper, the configurable computing architecture based on the homogeneous computing structure is proposed.The architecture consists of two types of matrices:the matrix of computing cells and the matrix of storage cells.The processor cell performs computations and has a transit communication channel to transmit the information without changes.The storage cell is used to store the inter-mediate computation results.The processor cell can execute following simple operations:addition of two one bit numbers;logical multiplication;logical multiplication with inversion;modulo2addition;memorizing”1”;microcon-stant generation.The process of homogeneous computing structure tuning and the process of computation are shared in a time.In the beginning,a stream of instructions is formed for HCS tuning and is written to the instruction registers of every computing and storage cell,and after that the stream of data is entered.Dur-ing data execution the instruction codes are kept in the instruction registers of computing and storage cells.Entry of the instruction codes is carried out through the chains of instruction code ports using the control signal.When computational requirements are changed,the new stream of instruction codes is entered to the structure swapping hardware configuration of the HCS.Thus,Parallel Image Processing on Configurable Computing Architecture153 it is possible to execute a series of tasks in rapid succession.One clock generator synchronizes the work of all cells of HCS and HSS. The information streams from the input device applied to the information inputs of the computing and storage matrices are processed in accordance with the instruction codes,moving synchronously with the clock cycles from one cell to another in the matrices of computing and storage structures.The system has not a control block,a main memory and a data bus.The main peculiarity of the organization of computing process on the HCS is the con-formity of specialized processor or,in the other words,microprogram module to each program instruction written in the program language of homogeneous computing structure.Many data streams are processed simultaneously,both in parallel and pipeline modes.Data streams may split,merge,exchange or cross information.This is called multipipelined execution.It is,therefore,im-portant to represent signal and image processing algorithms in multipipelined form to be properly mapped onto the homogeneous computing structure The homogeneous computing structure is tuned on a given data processing algorithm by storing of an appropriate sixteen bits code to the program register of the computing cell,which is an ordinary shift register.To speed up the tuning process,it is necessary both to rise the frequency of clock pulses and to increase the number of gates for the entry of program codes.3Construction of a microprogram module for ad-dressfindingFor the implementation of a microprogram module for addressfinding of the maximal or minimal number from two numbers,the module max-min(L,M) operation can be used.Consider this module.The algorithm for design of the module can be represented as follows:1.The determination of the difference V=L-M;2.The application of the operation of”memorizing of1”,the sign of thenumber V is memorized,that is,a value is formedQ= 11..11if Z=1(M>L) 00..00if Z=0(L≥M)where Z is a value of the elder sign bit(the n-1bit in modified compli-mentary code)3.The formation P=Q∧L,R=Q∧M;154 A.Lutsyk,O.Lutsyk,O.Pelenskyy4.The formation P =Q∧L,R =Q∧M;5.max(L,M)=P⊕R;6.min(L,M)=P ⊕R ;Only four steps are added in the algorithm for the construction of the microprogram module for addressfinding of the maximal and minimal numbers from two numbers.7.The formation D=Q∧H,F=Q∧G;8.The formation D =Q∧H,F =Q∧G;Here H and G are addresses of the numbers L and M respectively.9.(H,G)max(L,M)=D⊕F;10.(H,G)min(L,M)=D ⊕F ;Figure1:The microprogram module of max-min(L,M)operationThe task of the microprogram module max-min(L,M)is the determination of a larger and smaller number from two numbers and passing the larger number to one output and the smaller number to another output(Fig.1).The microprogram module of max-min(L-M)can be used in medianfiltering of images or mean trimmedfiltering of images,where is applied as an element for parallel-pipeline exchange sorting.Instead of,the microprogram moduleParallel Image Processing on Configurable Computing Architecture155 offinding address of the maximal and minimal number from two numbers, additionally to the function offinding the larger and smaller number and directing them to appropriate addresses,links larger and smaller numbers with its addresses,which they had on the inputs of the module,and directs these addresses together with the numbers to appropriate outputs.For example, assume that the larger number entered the module input with the address 2,and the smaller number with the address1.Then on the output of the module,which is determined for the output of the larger number,the address 2of the larger number will appear together with the larger number,and on the output,which is determined for the smaller number,the smaller number will appear together with its address1.Such module,using the scheme of exchange sorting can be used,for example,forfinding an area address with minimal value of absolute or standard deviation which corresponds to the most homogeneous region of a window.Figure2:Microprogram module offinding address of the maximal and minimal number from two numbersFor effective processing of numbers in the homogeneous structure which enter the module in bitwise sequence,a modified supplementary code is used. The modified supplementary code is as follows:n,n-1,n-2,n-3,...,2,1,where156 A.Lutsyk,O.Lutsyk,O.Pelenskyy n is a buffer bit,n-1,n-2are sign bits(n-1is thefirst sign bit),and n-3, (2)1are bits of a mantissa.Two numbers and the constant K1enter the module inputs in bit-by-bit sequences starting from the low-order bits and the result appears at the output also starting from low-order bits.Bitwise numbers enter the module and are processed there one by one without any intervals.Consider the work of the microprogram module offinding address of max-imal or minimal number(Fig.2).The size of this module is7×9cells.It exploits33computing cells more compared to the module max-min(L,M).According to the algorithm written above,the determination of the differ-ence V=L-M of two numbers is carried out in thefirst step.This function is performed by a group of the cells(3,1),(3,2),(4,1),(4,2),(5,1),(5,2).The cells(4,1),(5,1)and(5,2)handle the operation of sign change of the number M.The algorithm of the operation of sign change is in its turn divided into three steps.•Inverting of the constant K1(K1=00...001and has the same length as all processed numbers);•Modulo2addition of the number M and the inverted constant K1, displaced on the one cycle ahead;•Addition of the result of the previous step and the constant K1.The cell(3,1)inverts the constant K1.Modulo2addition is handled in the cell(5,1)and the result of this operation is added to the constant K1in the cell(5,2).The operation of addition of numbers L and(-M)with taking into account carry bit,is performed in the cell(3,2).The cell(4,3)carries out the next step of”memorizing”sign of the number V.The number V enters the input2of the cell(4,3)in the eighth cycle and the constant K1enters the input4in the sixth cycle.Since,the constant K1outruns the number V,then the value of high sign bit of the number V coincides with bit of the constant K1which has value1.In Table1,the number sequence,which enters the input2of the cell(4,3),and a sequence of the constant K1on the input4of the same cell are presented.Two numbers 0ZZa n−3..a1and0Z Z a n−3..a 1enter consecutively the input2.The constant, which is displaced in two cycles ahead,enters the input4.That is,constant bit to have value1enters the cell simultaneously with thefirst sign bit Z of thefirst number in the eighth cycle and with thefirst sign bit Z of the second number in the(n+8)th cycle.Therefore,the value of thefirst sign bit Q will appear in the output of the cell(4,3)after2cycles,and it will be kept by the cell for n cycles to the moment of arrival of thefirst sign bit of the second number and respectively of bit with value1of the constant K1.Parallel Image Processing on Configurable Computing Architecture157 Table1.The number V enters the input2of the cell(4,3)in the eighth cycle and the constant K1enters the input4on the sixth cycle.Since,the constant K1outruns the number V,then the value of high sign bit of the number V coincides with bit of the constant K1which has value1.Cycle78910...n+6n+7n+8n+9n+10...2n+6 Input20Z Z a n−3...a10Z Z a n−3...a 1 Input40100...00100 0The third step of formation P=Q∧L,R=Q∧M is carried out in the cells (4,6)and(4,5),respectively.P =Q∧L and R =Q∧M are formed in the cells(3,4)and(4,5)in the fourth cycle to be tuned on the operation”logical multiplication”.The maximal value of two numbers max(L,M)=P⊕R is determined in the cell(5,6)using modulo2addition of values P and R formed previously. The same operation offinding minimal value of two numbers min(L,M)= P ⊕R is performed in the cell(3,5).In the seventh step,values D=Q∧H and F=Q∧G are formed in the cells(2,7)and(4,8)for furtherfinding an address of the maximal number. The cells(4,9)and(6,8)are used for the formation of values D =Q∧H, F =Q∧G.And,at last,the address of the larger number(H,G)max(L,M)=D⊕F is determined in the cell(2,8)and the address of the smaller number (H,G)min(L,M)=D ⊕F is determined in the cell(6,9).As it can be seen from Fig.2,the larger number leaves the module from the output of the cell(5,9)in the seventeenth cycle,and the smaller number leaves the module from the output of the cell(2,9)in the nineteenth cycle. The address of the larger number appears in the output of the module from the output of the cell(2,9)and the address of the smaller number will be in the output of the module in the eighteenth cycle(cell(6,9)).4Implementation of trimmed meanfilterIt is known that the meanfiltering is better for suppression of normal white noise in image and the medianfilter is better for the elimination of impulsive noise.The medianfilters preserve sharp edges well,but they may course some distortion to corner edges.Trimmed meanfilter combines better properties of these twofilters and can be used for imagefiltering with a mixed conditional distribution.Further generalization can be achieved using so-called(α,β)-trimmed meanfiltering[6],where the parameterαdetermines the percentage158 A.Lutsyk,O.Lutsyk,O.Pelenskyy of the retained pixels and the parameterβis the shift of the subset of sorted pixels to be averaged.The inner trimmed meanfilter is more robust against outliers(impulsive noise)and the outer trimmed meanfilter is better in the case of uniform noise distribution.The outer trimmed meanfilter rejects central pixels in the ordered sequence and averages remaining pixels of this sequence.Figure3:Implementation of parallel-pipeline sorting of2N+1numbers on the homogeneous computing structureConsider the implementation of the trimmed mean imagefilter on the ho-mogeneous computing structure.The homogeneous storage structure is used for the formation of running window W(i,j).Pixels are delayed for several lines according to the size of a window(for the window3x3,for instance,the delay is carried out on one and two lines).In the next stage,all window elements are ordered.Microprogram modules of operation tofind the maximum of two numbers(min/max)are used for fulfilment of the ordering operation.This module performs sixteen-bits numbers.The module has two outputs:thefirst one corresponds to the greater number and the second one to the less number.A parallel-pipelined version of sorting can be used.Fig.3shows the diagram of the parallel-pipelined version of exchange sorting of2N+ing this approach,real time can be reached for the sorting operation.If pixels from local area W with the center in a point(i,j)are processed in the step k of the parallel-pipelined exchange sorting algorithm,then the pixels from area W with the center in point(i,j-1)are performed simultaneously on the step k-1of algorithm,the pixels from area W with the center in point(i,j-2)are processed in the step k-2and etc.In the next stage,the averaging of a subset of the ordered pixel sequenceParallel Image Processing on Configurable Computing Architecture159Figure4:Averaging of sorted sequence for implementation of trimmed mean filteris calculated,which depends from the trimmed meanfilter type:inner,outer, left,right.Fig.4shows the microprogram modules of addition which fulfils addition in parallel-pipelined mode for a pixel sequence in real time and the microprogram module of division,where divider is equal to the number of pixels of the sequence taken.As far as a small number of cells is needed for the construction of the microprogram module of addition,it is possibility to realize simultaneously all four types of trimmed meanfiltering and to switch for one or anotherfilter according to the type of the conditional distribution of noise.5Implementation of structure-adaptivefiltering and segmentationThe classical structure-adaptive algorithm operates on some image area which is called a window.The window is divided onto smaller subareas which are partly overlapped one with another and contains the central element of the window to be changed on new value,calculated according to its neighbour-hood.By a measure of homogeneity of the subarea may serve some local statistical properties,such as mean square deviation or mean absolute devi-ation.As far as these functions should be calculated for every pixel and the subarea with minimal value should be found,so it is clear,that these proce-dures require considerable computational cost.Fig.5shows the scheme of the implementation of structure-adaptivefil-tering or segmentation on the homogeneous computing structure.There are160 A.Lutsyk,O.Lutsyk,O.PelenskyyFigure5:Implementation of structure-adaptivefiltering on the homogeneous computing structurefollowing basic units:the former of a running window and subareas(FRW); calculator of mean square or absolute deviation(CMSD);address calculation of the minimal value of deviation(ACMD);delay lines(DL);the switcher;the filtering unit(FU).For implementation of structure-adaptive algorithms,the size of the win-dow is ordinary chosen5×5or more.The homogeneous computing and storage structure allows to form such running windows and organize the parallel ac-cess to all elements of the window to be able simultaneously calculate their statistical properties.The number of units CMSD is equal to the number of subareas within the running window to meet real time requirements.All these units work in paral-lel and their results should be brought to the next unit ACMD simultaneously.Algorithm of exchange sorting is used forfinding the minimal value of mean square or mean absolute deviation from n structural subareas.Since, after all,it does not need a value of minimal square deviation,but only the address of image subarea in which this value is detected,so the microprogram module of min/max is exposed to modification and a path for a string with an address of a subarea(from1to n)is inserted to microprogram module.This string is toughly tied to the value of mean square or mean absolute deviation and circulates with it according to the scheme of sorting.The scheme of exchanged sorting can be exposed the shortage,as long asParallel Image Processing on Configurable Computing Architecture161Figure6:Scheme of exchange sorting with a different order of turn of min/max microprogram modules and memory modules162 A.Lutsyk,O.Lutsyk,O.Pelenskyy it computes only the minimal value of deviation.As it can be seen infig.6, where two schemes of exchanged sorting are shown,they differ by the order of turn for min/max microprogram modules and memory modules.In Fig.6a the scheme of exchanged sorting is shown which is begun in the top left hand corner from the min/max microprogram module and ended in the lower left hand corner with the memory module.This scheme enables to economize more computing cells forfinding minimal value(approximately40%)than scheme shown in Fig.6b(stroked modules may be moved).But,for detection of the maximal value,the scheme shown in Fig.6b is better.Delay lines are used for compensation of delay caused by units CMSD and ACMD.Pixels of the subarea with the minimal value of deviation are passed to thefiltering unit to befiltered by the trimmed meanfilter which has been described in previous section.6ConclusionIn the paper,the reconfigurable computing architecture based on the homoge-neous computing structure concept with possibility of hardware modification in response to changes in processing environment and tuning on implementa-tion offiltering and segmentation algorithms in real time is presented.Con-figurable computing systems based on the homogeneous computing structure have some essential advantages over configurable computing systems based on field programmable gate arrays,in particular for that algorithms,which can be represented in parallel-pipeline mode.Using the configurable architecture,it will be able to carry out computa-tions cheaper,faster and consuming less power,so this device can be embedded in a personal computer or workstation and applied for image processing and video compression operations in real time using rapid hardware reconfigura-tion.AcknowledgementThis work was supported in part by the project agreement1931from the Science and Technology Center in Ukraine.References[1]J.Villasenor,B.Schoner,K.-N.Chia,C.Zapata,H.J.Kim,C.Jones,nsing and B.Mangione-Smith,Configurable computing solutionsParallel Image Processing on Configurable Computing Architecture163 for automatic target recognition,4th IEEE Symposium on FPGA’s for Custom Computing Machines(1996),70-79.[2]A.DeHon,The density advantage of configurable computing,Computer4,(2000),41-49.[3]M.Ya.Bartish,M.P.Bogachev et al.,Parallel Data Processing.Volume3:Computer Systems,Structures and Media for Solving High-dimensional Problems,Naukova Dumka,Kyiv,(1986),P.288.[4]A.Yu.Lutsyk,Medical image processing and compression on homoge-neous computing structure,54th ICB Seminar,Multimedia,Data Inte-gration,Medical Databases,Warsaw,(1999),31-32.[5]A.Yu.Lutsyk,B.V.Kisil’and O.L.Pelenskyy,Image processing in realtime on configurable computing architecture,The Third International Conference on Digital Information Processing and Control in Extreme Situations,Minsk,(2002),124-129.[6]R.M.Palenichka,P.Zinterhof,Yu. 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