Image Analysis and Computer Vision1996

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计算机机器视觉简历模板

计算机机器视觉简历模板Computer Vision Resume Template.Personal Information.Name: [Your Name]Email: [Your Email Address]Phone: [Your Phone Number]LinkedIn: [Your LinkedIn Profile URL]Objective.To obtain a challenging and rewarding position in the field of computer vision where I can apply my expertise in developing and implementing innovative solutions to complex problems.Skills.Image Processing and Analysis.Machine Learning and Deep Learning.Computer Vision Algorithms.Object Detection and Recognition.Scene Understanding and Segmentation.Image Manipulation and Enhancement.Python, C++, and CUDA.OpenCV, TensorFlow, and PyTorch.Experience.Computer Vision Engineer, ABC Company, [Start Date] [End Date]Developed and implemented computer vision models for real-time object detection and tracking.Designed and trained deep learning models using TensorFlow and PyTorch.Collaborated with a team of engineers to integrate computer vision solutions into production systems.Research Assistant, University of XYZ, [Start Date] [End Date]Conducted research on object recognition and image classification using deep learning techniques.Published papers in peer-reviewed conferences and journals.Presented research findings at academic conferences.Education.Master of Science in Computer Science, University of XYZ, [MM/YY] [MM/YY]GPA: [Your GPA]Bachelor of Science in Computer Science, ABC University, [MM/YY] [MM/YY]GPA: [Your GPA]Projects.Object Detection and Tracking System: Developed areal-time object detection and tracking system using YOLOv5 and OpenCV, achieving an accuracy of over 95%.Image Classification Model: Trained a deep learning model using TensorFlow to classify images into different categories with an accuracy of 98%.Self-Driving Car Simulator: Built a self-driving carsimulator using Python and OpenCV to test and evaluate computer vision algorithms for autonomous driving.Certifications.Certified Computer Vision Professional (CCVP)。

生物医学工程-东北大学中荷生物医学与信息工程学院

生物医学工程（083100）学科一、学科简介生物医学工程学科是在将信息科学和技术应用于解决医学实际问题中孕育和快速发展壮大起来的新兴交叉学科。

1996年6月获得国家一级学科学士学位授予权，1998年6月获得国家一级学科硕士学位授予权，2006年1月获得国家一级学科博士学位授予权，2008年3月成为省一级重点学科。

本学科拥有世界一流的医疗产业平台和医疗临床资源，发挥多学科交叉的优势，遵循应用基础研究和高技术发展研究相结合的原则，强调医学影像学的核心地位，开展医学成像科学与技术、医学图像分析与智能辅助、生物医学电子学、生物信息学和医学信息学等方向的研究，形成了独有的生物医学工程的教学、科研和开发综合性学科环境。

多年来，承担了多项国家级的科技攻关任务，包括国家863计划课题、国家科技攻关计划课题和国家自然科学基金项目，研制和开发了一系列先进的具有自主知识产权的数字医疗产品和技术。

本学科现有教授18人（其中博士生导师6人），副教授21人，讲师16人，其中32名教师具有博士学位。

形成了一支以著名教授为学科带头人，高水平的中青年博士为学术中坚，归国留学人才不断加盟的高层次学术梯队。

二、培养目标生物医学工程学科旨在为尖端的医疗技术领域培养高端人才。

具体目标为：（一）学习马列主义毛泽东思想，拥护共产党的领导，拥护社会主义制度，热爱祖国，遵纪守法，具有良好的道德品质，积极为社会主义建设服务。

（二）掌握生物医学工程学科坚实宽广的基础理论和深入系统的专门知识。

（三）具有独立从事科学研究工作的能力。

（四）在本学科领域取得一定的创造性成果。

三、学习年限与学分要求全日制攻读博士学位,学习年限原则上为3年；在职攻读博士学位,学习年限原则上为4年,但无论全日制还是在职攻读博士学位,保留学籍时间不超过6年。

学分要求：最低学分10学分。

四、研究方向（一）医学成像科学与技术本研究方向聚焦于医学成像系统的原理和设备的研究，具体包括X-射线、CT、MRI、PET、超声等现代医学影像设备的原理和技术。

图像拼接算法及实现.doc

图像拼接算法及实现（一）来源：中国论文下载中心 [ 09-06-03 16:36:00 ] 作者：陈挺编辑：studa090420 论文关键词：图像拼接图像配准图像融合全景图论文摘要：图像拼接(image mosaic)技术是将一组相互间重叠部分的图像序列进行空间匹配对准,经重采样合成后形成一幅包含各图像序列信息的宽视角场景的、完整的、高清晰的新图像的技术。

图像拼接在摄影测量学、计算机视觉、遥感图像处理、医学图像分析、计算机图形学等领域有着广泛的应用价值。

一般来说,图像拼接的过程由图像获取,图像配准,图像合成三步骤组成,其中图像配准是整个图像拼接的基础。

本文研究了两种图像配准算法:基于特征和基于变换域的图像配准算法。

在基于特征的配准算法的基础上,提出一种稳健的基于特征点的配准算法。

首先改进Harris角点检测算法,有效提高所提取特征点的速度和精度。

然后利用相似测度NCC(normalized cross correlation——归一化互相关),通过用双向最大相关系数匹配的方法提取出初始特征点对,用随机采样法RANSAC(Random Sample Consensus)剔除伪特征点对,实现特征点对的精确匹配。

最后用正确的特征点匹配对实现图像的配准。

本文提出的算法适应性较强,在重复性纹理、旋转角度比较大等较难自动匹配场合下仍可以准确实现图像配准。

Abstract：Image mosaic is a technology that carries on the spatial matching to a series of image which are overlapped with each other, and finally builds a seamless and high quality image which has high resolution and big eyeshot. Image mosaic has widely applications in the fields of photogrammetry, computer vision, remote sensing image processing, medical image analysis, computer graphic and so on. 。

模糊聚类及其在图像分割中的应用

密级：学校代码：10075分类号：学号：20061000工学硕士学位论文模糊聚类及其在图像分割中的应用学位申请人：曹铮指导教师：李昆仑教授副指导教师：刘明副教授学位类别：工学硕士学科专业：通信与信息系统授予单位：河北大学答辩日期：二○一○年六月Classified Index: CODE: 10075 U.D.C: NO: 20061000A Dissertation for the Degree of Master Fuzzy Clustering and the application on Image SegmentationCandidate:Cao ZhengSupervisor:Prof. Li KunlunAssociate Supervisor Associate Prof. Liu Ming Academic Degree Applied for: Master of EngineeringSpecialty: Comm. &Info. SystemUniversity:Hebei UniversityDate of Oral Examination:June, 2010摘要图像分割是指把图像分为各具特性的不重叠区域以提取出感兴趣目标的技术和过程，是数字图像处理技术中的关键技术之一，也是计算机视觉中的一个经典问题。

图像分割是对图像进行分析理解的基础，在计算机视觉、模式识别、目标跟踪和医学图像处理等领域已经得到了广泛应用。

由于图像在成像过程中受到各种因素的影响，导致待提取目标和背景之间具有一定的相似性和不确定性，而模糊理论和模糊图像处理技术适合于处理这种带有不确定性的问题。

模糊聚类方法是处理图像分割问题的一个重要理论分支。

目前在实际应用中广泛使用的是模糊C-均值（Fuzzy C-means, FCM）算法，它将聚类归结为一个带有约束的非线性规划问题，通过对目标函数的优化求解获得数据集的模糊划分。

医学图像配准技术综述

医学图像配准技术A Survey of Medical Image Registration张剑戈综述，潘家普审校（上海第二医科大学生物医学工程教研室，上海 200025）利用CT、MRI、SPECT及PET等成像设备能获取人体内部形态和功能的图像信息，为临床诊断和治疗提供了可靠的依据。

不同成像模式具有高度的特异性，例如CT通过从多角度的方向上检测X线经过人体后的衰减量，用数学的方法重建出身体的断层图像，清楚地显示出体内脏器、骨骼的解剖结构，但不能显示功能信息。

PET是一种无创性的探测生理性放射核素在机体内分布的断层显象技术，是对活机体的生物化学显象，反映了机体的功能信息，但是图像模糊，不能清楚地反映形态结构。

将不同模式的图像，通过空间变换映射到同一坐标系中，使相应器官的影像在空间中的位置一致，可以同时反映形态和功能信息。

而求解空间变换参数的过程就是图像配准，也是一个多参数优化过程。

图像配准在病灶定位、PACS系统、放射治疗计划、指导神经手术以及检查治疗效果上有着重要的应用价值。

图像配准算法可以从不同的角度对图像配准算法进行分类[1]：同/异模式图像配准，2D/3D图像配准，刚体/非刚体配准。

本文根据算法的出发点，将配准算法分为基于图像特征(feature-based)和基于像素密度(intensity-based)两类。

基于特征的配准算法这类算法利用从待配准图像中提取的特征，计算出空间变换参数。

根据特征由人体自身结构中提取或是由外部引入，分为内部特征(internal feature)和外部特征(external feature)。

【作者简介】张剑戈（1972-），男，山东济南人，讲师，硕士1. 外部特征在物体表面人为地放置一些可以显像的标记物（外标记，external marker）作为基准，根据同一标记在不同图像空间中的坐标，通过矩阵运算求解出空间变换参数。

外标记分为植入性和非植入性[2]：立体框架定位、在颅骨上固定螺栓和在表皮加上可显像的标记。

国内外期刊类别

一、国内期刊：一级期刊：电子学报（英文版收录SCI）通信学报软件学报计算机学报（英文版收录SCI）Journal of Computer Science and Technology自动化学报物理学报(SCI收录)中国科学科学通报二级期刊：信号处理类：生物医学工程学报、信号处理、数据采集与处理、电子与信息学报、系统仿真学报计算机类：计算机工程与应用、计算机工程、计算机应用、计算机科学图形图像类：中国图形图像学报、计算机辅助设计与图形学学报电子测量类：仪器仪表学报、传感技术学报、测试技术学报、电子仪器与测量学报、信息与控制、模式识别与人工智能、机器人、电子技术应用二、国外期刊：ANNUAL REVIEW OF BIOMEDICAL ENGINEERINGJournal of Neural EngineeringIEEE TRANSACTIONS ON BIOMEDICAL ENGINEERINGIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERINGIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERINGIEEE SIGNAL PROCESSING MAGAZINEIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCECOMPUTATIONAL INTELLIGENCECOMPUTER VISION AND IMAGE UNDERSTANDINGIEEE Computational Intelligence MagazineIEEE INTELLIGENT SYSTEMSIEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERINGBiomedical Signal Processing and ControlBIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONSSIGNAL PROCESSINGIEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE IEEE SIGNAL PROCESSING LETTERSJOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH NEUROCOMPUTING。

国际会议级别

Asian Control Conference (ASCC)
European Association for Signal Processing 18.
(EURASIP)
European Signal Processing Conference (EUSIPCO)
19. European Graphics Society
The Optoelectronics and Communications Conference (OECC)光電與通訊工程國際研討會
International Symposlum on Growth of
19. Association for "Optoelectronics Frontier by Nitride Ⅲ-Nitrides(ISGN)三族氮基半導體生長國際研討
23. European Union Control Association (EUCA)
European Control Conference (ECC)
Innovative Computing, Information and Control 24.
(ICIC)
International Symposium on Intelligent Informatics (ISII)
6. Society (WSEAS)
八)
Administered by UCMSS Universal Conference The International Conference on e-Learning,
7. Management Systems & Support/The University of e-Business, Enterprise Information Systems, and

人脸表情识别英文参考资料

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边缘特征提取及其在图像匹配中的应用研究

本科毕业设计论文题目边缘特征提取及其在图像匹配中的应用研究专业名称学生姓名指导教师毕业时间2014年6月任务书一、题目边缘特征提取及其在图像匹配中的应用研究二、指导思想和目的要求本题目来源于科研，主要学习图像特征的概念及边缘特征的提取，研究常用的图像匹配算法，进而实现相关算法。

希望通过该毕业设计，学生能达到：1．利用已有的专业知识，培养学生解决实际工程问题的能力；2．锻炼学生的科研工作能力和培养学生团队合作及攻关能力。

三、主要技术指标1．学习图像特征中边缘特征的提取；2．掌握基于边缘特征的图像匹配算法；3．实现边缘特征的提取及其在图像匹配中的应用。

四、进度和要求第01周----第02周：参考翻译英文文献；第03周----第04周：学习常用的图像特征及其边缘特征的提取方法；第05周----第08周：研究基于边缘特征的图像匹配算法；第09周----第14周：编写基于边缘特征的图像匹配算法程序；第15周----第16周：撰写毕业设计论文，论文答辩。

五、主要参考书及参考资料1. 尼克松. 特征提取与图像处理. 电子工业出版社.2. 李言俊.景象匹配与目标识别技术. 西北工业大学出版社.3. 梁建宁. 特征选取与图像匹配. 复旦大学硕士学位论文.4. 叶耘恺. 基于边缘特征的图像配准方法研究. 重庆大学硕士学位论文.学生指导教师系主任摘要图像匹配（Image Matching）是计算机视觉和图像处理领域中一项非常重要的工作。

图像匹配技术是实现图像融合、图像校正、图像镶嵌以及目标识别与跟踪的关键步骤之一，已经广泛应用在图像识别以及图像重建等领域中。

简单来说，图像匹配就是找到两幅不同图像之间的空间位置关系。

图像匹配主要可分为基于灰度的匹配和基于特征的匹配。

本文首先对现有图像匹配的方法进行分类、概括和简要的说明；分析了课题研究的背景，以及对国内外图像匹配的研究状况描述；其次对现有的图像匹配的几种常见算法进行简要说明，其中着重介绍了基于边缘特征的匹配算法。

遥感影像配准方法探讨

科技信息2010年第7期SCIENCE&TECHNOLOGY INFORMATION遥感是目前为止能够提供全球范围的动态对地观测数据的惟一手段,其成像模式多种多样。

多源传感器影像的数据融合,可以产生出比单一信息源更精确、更完整、更可靠的影像信息。

在融合这些多源遥感影像数据时,必须先进行影像配准，经过适当配准的多传感器图像可以在像素级直接融合形成融合图像，然后在此基础上完成目标探测、特征提取和目标识别等处理。

图像配准广泛应用于航空航天技术、地理信息系统、图像镶嵌、图像融合、目标识别、虚拟现实等领域。

1图像配准的基本概念图像配准是指同一目标的两幅（或者两幅以上）图像在空间位置上的对准，图像配准的技术过程称为图像匹配。

影像匹配实质上是在两幅(或多幅)影像之间识别同名点,是计算机视觉及遥感数字图像制图的核心问题[1]。

对影像匹配可作如下数学描述[2]:若影像I1与I2中的像点O1与O2具有坐标P1=(x1,y1)、P2=(x2,y2)及特征属性f1与f2，即O1=(P1,f1)、O2=(P2,f2)。

其中f1与f2可以是P1与P2为中心的小影像窗口的灰度矩阵g1与g2,也可以是其他能够描述O1与O2的特征。

基于f1与f2定义某种测度m(f1,f2)。

所谓影像匹配就是建立一个映射函数M满足：P2=M(P1,T)、M(f1,f2)=max或min(O1∈I1,O2∈I2)。

其中T为描述映射M的参数矢量,测度m表示O1与O2的匹配程度,称为匹配测度。

基于不同的理论或不同的思想可以定义各种不同的匹配测度,因而形成了各种影像匹配方法及相应的实现算法。

2图像匹配的一般算法2.1基于图像灰度的匹配方法基于图像灰度的匹配方法的基本思想是：首先对待匹配图像做几何变换；然后根据灰度信息的统计特性定义一个目标函数，作为参考图像与变换图像之间的相似性度量，使得匹配参数在目标函数的极值处取得，并以此为匹配的判决准则和匹配参数最优化的目标函数，从而将匹配问题转化为多元函数的极值问题；最后通过一定的最优化方法求得正确的几何变换参数。

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COMPUTERVISIONANDIMAGEUNDERSTANDINGVol.66,No.1,April,pp.33–93,1997

ARTICLENO.IV970602

SURVEYImageAnalysisandComputerVision:1996AzrielRosenfeldComputerVisionLaboratory,CenterforAutomationResearch,UniversityofMaryland,CollegePark,Maryland20742-3275

ReceivedJanuary17,1997

one,whichwillappear(ifitdoes)intheyear2000.Ihopethecommunityhasfoundmybibliographiesuseful,andThispaperpresentsabibliographyofnearly2150references

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tion,neuralnetworks,artiﬁcialintelligenceandpatternrecogni-primarilyitemspublishedduring1996.Thecoverageis

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ingthemsoon.ThedatabaseofreferencesfromwhichI(I)Three-dimensionalrecoveryandanalysis

preparethebibliographieseachyear,whichIbeganin(J)Three-dimensionalshape

1961,hasjust(December1996)reached50,000items;it(K)Motion

currentlygrowsbyover2000itemsperyear.IdonotknowLetter/numbercodesinthetext(A.1,etc.)correspondtoifIwilleverstopcollectingreferences,butIwilldeﬁnitely

sectionsofthebibliography.stoppreparingannualbibliographies,perhapswiththis

Papers,books,etc.relatingprimarilytospeciﬁctopicsone(#27,thecubeof3),perhapswiththenextone(#28,

willbecitedinlatersections.Inthissectionweciterefer-aperfectnumber),andcertainlywhenIreachthe30th

encesthatrelatetomorethanonetopic:

(A.1)Meetingsandmeetingproceedings:[1–45]ThesupportoftheOfﬁceofNavalResearchunderGrantN00014-95-(A.2)Books[46–48];papercollections[49–52];journal1-0521(DARPAOrderNo.C635)isgratefullyacknowledged,asisthe

helpofSandyGermanandJanicePerroneinpreparingthisbibliography.specialissuesandsections[53–55];twonewjournals[56–

57];papersandjournalspecialissuesonresearchatspeciﬁc(F.4)Skeletonsandthinning;distance:[1176–1203](F.5)Pattern(pathplanning,etc.):[1204–1229];formalinstitutions[58–79];generalpapers[80–81];andtheprevi-ousbibliographyinthisseries[82].languages:[1230–1232].

B.RELATEDTOPICSG.LIGHTNESSANDCOLOR;TEXTURE

Thefollowingrelatedareasarenotcoveredsystemati-(G.1)Lightness,polarization,andcolor:[1233–1287]

cally,butwegiveafewreferencesonthem:(G.2)Texture:modelingandsynthesis[1288–1323]

(G.3)Texture:description:[1324–1357](B.1)Geometryandgraphics:[83–127]

(G.4)Texture:segmentation:[1358–1389].(B.2)Compressionandprocessing:[128–170]

(B.3)Sensorsandoptics:[171–189](B.4)Visualperception:[190–205]H.MATCHING;STEREO

(B.5)Neuralnetworks:[206–221](H.1)Imageandtemplatematching:[1390–1461](B.6)Artiﬁcialintelligenceandpatternrecognition:

(H.2)Houghtransforms:[1462–1503];structurematch-[222–238].

ing:[1504–1515];recognition[1516–1536](H.3)Stereo,etc.:[1537–1613].C.APPLICATIONS

(C.1)Documents:[239–250]*I.RANGE;RECOVERY

(C.2)Biomedicalandbiological:[251–261]*

(C.3)Human:[262–383](I.1)Rangesensingandrangedataanalysis:[1614–