Pattern Recognition Group
模式识别与机器学习第一章
结构模式识别
• 该方法通过考虑识别对象的各部分之间的联 系来达到识别分类的目的。
• 识别采用结构匹配的形式,通过计算一个匹 配程度值(matching score)来评估一个未知 的对象或未知对象某些部分与某种典型模式 的关系如何。
• 当成功地制定出了一组可以描述对象部分之 间关系的规则后,可以应用一种特殊的结构 模式识别方法 – 句法模式识别,来检查一个 模式基元的序列是否遵守某种规则,即句法 规则或语法。
• 图像处理 • 计算机视觉 • 人工智能 • 数据挖掘 • 控制论
……
教学方法
• 着重讲述模式识别与机器学习的基本概 念,基本理论和方法、关键算法原理以 及典型应用情况。
• 注重理论与实践紧密结合
–实例教学:通过实例讲述如何将所学知识 运用到实际应用之中
• 尽量避免引用过多的、繁琐的数学推导。
ቤተ መጻሕፍቲ ባይዱ
教学目标
Applications, Springer, New York, USA, 2002. • Christopher M. Bishop (2006),Pattern Recognition and Machine
Learning,Springer. • Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2001),The
期。研究的是以40年代兴起的神经网络模型为理论基础的“没
有知识”的学习。模式识别发展的同时形成了机器学习的两种 重要方法:判别函数法和进化学习
• 第二阶段是在60年代中叶至70年代中叶,被称为机器 学习的冷静时期。研究的目标是模拟人类的概念学习阶段,
并采用逻辑结构或图结构作为机器内部描述。神经网络学习机 因理论缺陷转入低潮。
专业英语 人工智能 最终版
• 人工智能是计算机科学的前沿,充满机遇和挑战。 • “A student in physics might reasonably feel that all the good ideas have already been taken by Galileo,Newton,Einstein,and the rest,and that it takes many years of study before one can contribute new ideas,AI,on the other hand,still has openings for a full-time Einstein.” • _______《Artificial Intelligence:A modern Approach》 • (一位在物理学领域的学生会理所当然的认为所有的好点子已经 被伽利略,牛顿,爱因斯坦和其他人,它需要许多年前的研究能 做出贡献的新思路,另一方面,AI,仍然作为一个全职的爱因斯 坦。)
Definition 定义
Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create it. Definition in AI textbook :”the study and design of intelligent agents” 人工智能(AI)是机器智能与计算机科学,旨 在创建它的分支。在AI教科书的定义:“学习 的智能代理的设计。”
在解决问题方面:1960纽厄尔编译一个普遍的问题解决者 (GPS),可以解决11个不同类型的问题; 在专家系统:1968的结果费根鲍姆开发DENDRAL“专家系 统”并投入使用;
Pattern_Recognition
d2 A1
1 2
0 32 .52+ 6.52
A2
25 17 .52+1.52
A3
36+36 8 6.52+0.52
B1
9+4 5 3.52+4.52
B2
25+25 2 5.52+1.52
B3
16+36 4 4.52+0.52
C1
1+64 41 0.52+1.52
C2
4+1 13 2.52+5.52
3
神经网络
• 大规模并行计算 • 学习、推广、自适应、容错、分布表达 和计算
• 优点:可以有效地解决一些复杂的非线 性问题 • 缺点:取少有效的学习理论
模式识别应用
• • • • • • • • • • 文本分类 文本图像分析 工业自动化 数据挖掘 多媒体数据库检索 生物特征识别 语音识别 生物信息学 遥感 ….
29
C1
1+64
C2
4+1
36+36 9+4
25+25 16+36
9+4
1+64
9+9
1+9
16+36
0
1+1
58
1: A1 (2,10)
2:A3, B1,B2, B3, C2 (6, 6)
3: A2, C1 (1.5,3,5)
第二次迭代: 中心为1: (2,10), 2: (6,6), 3: (1.5,3.5)
• 决策 (Decision) • 学习 (Learning) • 普适、推广、概括(Generalization)
软件交互设计精髓 相关英文词汇
1, persona [pə:'səunə, -nɑ:]n. 人物角色;伪装的外表Persona: 角色|人格面具|人,人格Persona certa: 特定人Persona 5: 女神异闻录52, scenariosn. 情节;脚本;情景介绍(scenario的复数)scenarios: 脚本|小说的情节梗概|情景介绍qualitative scenarios: 定性情景|定性研究技术Scenarios Analysis: 幕景分析法|情景分析法|情境分析3, command vectorcommand vector: 命令矢量,命令向量|命令向量——允许用户向程序发起指令的特殊技术|命令向量command-set vector: 命令集向量vector command: 向量命令4, working set工作集;工作区;工作组working set: 工作集|工坐|工作组LERROR_WORKING_SET_QUOTA: 配额不足,无法完成请求的服务。
current working set: 当前工作页面组|现行工作集5, buttonsn. 纽扣;按钮(button的复数形式)buttons: 按钮|纽扣|广告large buttons: 放大按钮submit buttons: 提交按钮6, GUIabbr. 图形用户界面(graphical user interface)GUI: 图形用户界面|Graphical User Interface|图形用户接口JkDefrag GUI: 磁盘整理Xiao Gui: 小鬼|黄鸿升7, UNIXn. 一种多用户的计算机操作系统Unix: 用于服务器的一种操作系统|一种计算机操作系统|一种操作系统SCO UNIX: 安装大硬盘|服务器|台机器npasswd UNIX: 的一种代理密码检查器8, interface ['intəfeis]n. 接口;界面;接触面interface: 界面|分界面|接口分界面连接体User interface: 户界面|使用者介面|户接口physical interface: 物理接口|具体接口|实体介面9, context ['kɔntekst]n. 环境;上下文;来龙去脉context: 语境|上下文|关联菜单device context: 设备上下文|设备描述表|装置内容context window: 上下文窗口10, typography [tai'pɔɡrəfi]n. 排印;活版印刷术;印刷格式typography: 印刷术|排印工艺|凸版印刷fundamental typography: 美术字体设计基本原理letterpress typography: 活版11, composition [,kɔmpə'ziʃən]n. 作文,作曲;构成;合成物Composition: 构图|组成|写作Color Composition: 色彩构成|色彩构图|铯彩构图grain composition: 颗粒组成|粒度组成12, affordancen. 功能可见性;自解释性;给养Affordance: 功能可见性|可能性|供性Manual affordance: 手动启示13, visual ['vizjuəl]adj. 视觉的,视力的;栩栩如生的visual: 视觉的|视觉的,视力的|视觉visual field: 视野|视野或视界|视界Visual Basic: 计算机二级|级考试|的启动14, cue [kju:]n. 提示,暗示;线索vt. 给…暗示CUE: 选听开关|暗示|提示cue ball: (撞球中的)母球、主球|母球|主球cue in: 插入|告诉/动词词组|提供启示15, clutter ['klʌtə]n. 杂乱,混乱vt. 使凌乱;胡乱地填满clutter: 杂乱回波|杂乱|喧闹clutter reflections: 地物反射|地物反clutter filter: 杂波滤波器|反干扰滤波器|释义:静噪滤波器16, layering ['leiəriŋ]n. 压条法;分层;成层v. 分层而成;用压条法培植(layer的ing形式)layering: 层次|分层|成层layering capabilities: 分层性能lithologic layering: 岩性层理17, similarity [,simi'læriti]n. 类似;相似点similarity: 相似性|类似|相似geometric similarity: 几何相似|何相似similarity theory: 相似理论18, contrast [kən'trɑ:st, -'træst, 'kɔntrɑ:st, -træst]n. 对比;差别;对照物vi. 对比;形成对照vt. 使对比;使与…对照CONTRAST: 对比度|反差|副对比度contrast-: 对比|对比,对照luminance contrast: 辉度对比|亮度对比|亮度反差19, texture ['tekstʃə]n. 质地;纹理;结构;本质,实质Texture: 质地|肌理|质感texture swapping: 纹理交换Texture Modes: 材质模式20, baroquen. 巴洛克风格;巴洛克艺术adj. 巴洛克式的;结构复杂的,形式怪样的Baroque: 巴洛克式|巴洛克|巴罗克Baroque Pop: 巴洛克流行|巴洛克流行 )Baroque Art: 巴洛克艺术|巴洛克藝術|风格服饰21, contour ['kɔntuə]n. 轮廓;周线;等高线;电路;概要vt. 画轮廓;画等高线Contour: 轮廓|等高线|外形,轮廓,保持Contour farming: 等高耕作|等高种植|等高耕种,等高种植Contour map: 等高线图|等高线图,等值线图|轮廊投影仪22, leverage ['li:vəridʒ, 'le-]n. 杠杆作用;杠杆效率;手段,影响力leverage: 杠杆|财务杠杆率|杠杆作用,借贷机会leverage borrowings: 杠杆借款Leverage Effect: 杠杆效应|槓桿效果|槓桿效應23, type maker类型标记24, entry ['entri]n. 入口;进入;登记;条目;对土地的侵占;[商]报关手续entry: 排队终端入口|报关手续,报单,进入,入口|进入Order Entry: 订单输入|买卖盘输入|订单录入entry strategies: 进入战略25, group boxgroup box: 群组方块分组框|成组控制框|复选成组框group letter box: 组合式信箱|排列组合式信箱group-box control: 组方块控件群组方块控制项|组方块控件26, brand [brænd]n. 商标,牌子;烙印vt. 打烙印于;印…商标于;铭刻于,铭记Brand: 品牌|厂标显示|牌名individual brand: 个别品牌|独立品牌|别品牌brand recognition: 品牌识别|品牌认可/认同|品牌認知27, chromostereopsischromostereopsis: 释义:色彩实体视觉28, quicken ['kwikən]vt. 加快;鼓舞;使复活vi. 加快;变活跃;进入胎动期quicken: 加快|加速|锐折Quicken Spell: 法术瞬发|快速施法quicken up: 加速|加快|快起来29, intuition [,intju:'iʃən]n. 直觉;直觉的知识;直觉力intuition: 直觉知识,直觉|直观|本能language intuition: 语感|语言直觉intuition thinking: 直觉思维30, mental ['mentəl]n. 精神病患者adj. 精神的;脑力的;疯的mental: 心理的,精神的,智力的|精神的|记忆的,精神的,智力的mental process: 心理过程|心理历程|心理过程 [智力过程mental map: 心象地图|意象图|意境地图31, comparison [kəm'pærisən]n. 比喻;比较;对照;比较关系Comparison: 比较|对比|比较,对比key comparison: 键(码)值比较|关键比对comparison lamp: 比较灯|比侧灯|比测灯32, beat aroud the bush拐弯抹角33, resizableadj. 可变尺寸的;可调整大小的Resizable: 大小|可否改变视窗大小|大小可调Resizable thumbnails -: 可调整大小的缩略图Making a textbox resizable: 建立可变尺寸的文本框34, neat [ni:t]adj. 整洁的;未搀水的;优雅的;灵巧的;齐整的;平滑的Neat: 真整洁|整齐|全国中小学英语学习成绩测试neat line: 准线|内图廓线|界线Neat silk: 加捻丝线35, politicallyadv. 政治上politically: 政治上|政治|政治上,政策上politically unclear: 政治模糊Politically Correctness: 政治正确性|政治正确36, pickle ['pikl]n. 盐卤;泡菜;腌制食品vt. 泡;腌制pickle: 酸洗液|酸菜|盐水pickle brittleness: 酸洗脆性|氢脆|浸蚀脆性brine pickle: 卤水(腌制用)|盐腌液37, in a pickle[口语]非常混乱,乱七八糟;很脏;处境困难(或尴尬)in a pickle: 在一腌菜中图片|陷入困境|身陷困境in a pretty pickle: 处于困境be in a pickle: 为难38, take the red-eye39, grunge [ɡrʌndʒ]n. [美俚]蹩脚货;乏味的东西;难看的东西Grunge: 垃圾摇滚|垃圾|垃圾乐Post Grunge: 后垃圾|后垃圾 :texture grunge: 纹理蹩脚货图片40, radio button单选按钮radio button: 单选按钮|单选钮圆钮|选项按钮Radio Button: 单选钮圆钮Old Radio Button: 老圆按键图片41, close boxesClose boxes: 关闭框42, drop-down menu下拉式菜单drop-down menu: 下拉式菜单|下拉式菜单下拉式功能表|下拉菜单Drop-down menu): 下拉式选单image with a drop-down menu: 带图象的下拉菜单-43, comboboxn. 组合框;下拉列表框ComboBox: 组合框|控件|下拉框Icon combobox: 图标选择框-Combobox properties: 下拉式清单方块属性44, metaphor ['metəfə]n. 比喻说法;暗喻,隐喻metaphor: 隐喻|比喻|隐喻,比喻,比喻说法conceptual metaphor: 概念隐喻|概念隐喻理论|概念隱喻dead metaphor: 原义已消失的比喻|死隐喻|亡隐喻45, macintoshn. 苹果公司生产的一种型号的计算机;apple公司于1984年推出的一种系列微机;麦金托什机Macintosh: 麦金塔|麦金托什|苹果机Robert MacIntosh: 麦金托什Macintosh clone: 麦金塔计算机仿制品46, WYSIWYGabbr. 所见即所得(what you see is what you get)WYSIWYG: What You See Is What You Get|所见即所得|所见所得WYSIWYG WhatYouSeeBeforeYouGetIt: 先见后得WYSIWYG interface: 所见即所得界面47, primitive ['primitiv]n. 原始人adj. 原始的,远古的;简单的,粗糙的primitive: 基元|原始的|原语primitive streak: 原条|原条[见于原肠胚|原线,原绦primitive function: 原函数|基元功能|原生函数48, compound ['kɔmpaund, kəm'paund]n. 化合物;复合词;混合物vt. 混合;合成;和解妥协;搀合vi. 妥协;和解adj. 复合的;混合的compound: 混合料|化合物|混合物compound die: 合模|复合模|复式模计caulking compound: 填隙料|嵌缝填料|捻缝胶泥49, double-clicking ['dʌbl,klik]n. [计]双击double-clicking: 双击defaults for Double-clicking: 连按两下鼠标的预设|双击的默认|双击的默认连按两下滑鼠的预设50, click-and-draggingClick-and-dragging: 单击并拖动51, pushbuttonn. 按钮pushbutton: 电钮|按扭|选择按钮Pushbutton Switches: 按键开关|钮开关pushbutton time: 释义:按纽定时器52, check-boxes复选框53, hyper-links超级链接54, direct manipulation直接操作,直接操纵Direct manipulation: 直接操作|直接操纵|直接操控Direct manipulation animation: 真接绘制动画55, handle ['hændl]n. 柄;把手;手感;口实vt. 买卖;处理;操作;触摸;运用vi. 易于操纵;搬运handle: 手柄|锅把|锅耳control handle: 控制柄|控制手柄|操纵手柄handle ring: 手把环|把手环56, labeledadj. 有标签的;示踪的v. 贴标签于…;把…称为;[化]示踪(label的过去分词)labeled: 贴有(产品合格)标签的|贴有标签的|示踪的labeled door: 防火门labeled rating: 标定等级57, label ['leibl]n. 标签;商标;签条vt. 标注;贴标签于label: 标签|标记|行李标签luggage label: 行李标签|行包标签|行李签label disk: 标注磁盘|卷标磁盘|标签磁盘58, field ['fi:ld]n. 领域,牧场,旷野,战场,运动场adj. 扫描场,田赛的,野生的vi. 担任场外队员vt. 把暴晒于场上,使上场field: 字段,信息组,域|字段|域field winding: 磁场绕组励磁绕组|磁场绕组|励磁线圈field coils: 励磁线圈59, navigation bar导航条;导航栏navigation bar: 导航栏|导航条|导览列Breadcrumb Navigation Bar: 导览列60, boxes框61, iconsabbr. 核标准情报中心[美](information center on nuclear standards)Icons: 图标|图示|电脑图像Busan I'cons: 釜山偶像|釜山大宇small icons: 小图标|小图示|小图标列表62, control [kən'trəul]控件n. 控制;管理;抑制;操纵装置vt. 控制;管理;抑制control: 控制|管制| 控制Quality control: 质量控制|质量管理|品质控制control device: 控制手段|控制装置|控制器63, pane [pein]n. 窗格;窗格玻璃;边;面;嵌板vt. 装窗玻璃于;镶嵌板于pane: 窗格|窗玻璃|屏面diagram pane: 关系图窗格criteria pane: 条件窗格64, instruction [in'strʌkʃən]n. 指令,命令;指示;用法说明;教导instruction: 指令|教导|说明call instruction: 呼叫指令|得指令|调用指令packing instruction: 包装说明|包装要求|包装说伧65, demonstration [,demən'streiʃən]n. 示范;证明;示威游行demonstration: 示范|演示|实证Demonstration available: 可以进行演示|可以进行演示来源:考试大|没出国疑问停止演示demonstration meter: 演示用电表66, stylus ['stailəs]n. 唱针;铁笔;尖笔;药笔剂stylus: 触针|唱针|定位笔stylus tracer: 细探子描记器(牙科)|释义:细探子描记器,针形描绘器,细探子记录器(牙科)cutting stylus: 录音针|刻针|刻针 (录声刻纹用)67, human interface style guide人性化界面风格指南68, Phase [feiz]n. 相;位相;阶段vi. 逐步前进vt. 使定相;逐步执行phase: 相位|阶段|月相phase difference: 相差|相位差|相差,相位差design phase: 设计阶段|产品设计|設計階段69, capture ['kæptʃə]n. 战利品,俘虏;捕获vt. 俘获;夺得Capture: 影音采集卡|捕获|俘获Motion Capture: 动作捕捉|动态捕捉|动作资料截取capture velocity: 外部吸气罩|控制风速|截获速度70, termination [,tə:mi'neiʃən]n. 结束,终止Termination: 终止|结束|解雇,终止chain termination: 链终止|链终止反应|链终止作用termination indemnity: 解雇补偿金71, cursor ['kə:sə]n. (计算尺的)游标,指针;[计]光标cursor: 游标|光标|指示器,光标dynamic cursor: 动态游标|动态数据指针|动态光标cursor library: 光标库|游标库72, hintingv. 微调;隐示;暗示(hint的现在分词)Hinting: 字体微调|微调|文字描绘Type Hinting: 类型提示hinting technology: 提示技术73, captive ['kæptiv]n. 俘虏;迷恋者adj. 被俘虏的;被迷住的captive: 俘虏|被控制的|捕获captive balloon: 系留气球|系留气球交通captive test: 静态试验|静态试验,工作台试验(发动机、推进器等的)|试验台试验,静态试验74, CRTabbr. 阴极射线管(cathode ray tube)CRT: 阴极射线管|显像管|显示器crt display: 阴极射线管显示器|荧光屏显示器|映像管显示器CRT indicator: 阴极射线指示器75, fine mofor confrolFine mofor confrol: 手指精细运动控制76, gross motor controlGross motor control: 粗略运动控制77, mondayn. 星期一Monday: 今天是星期天/周一/周二/周三/周四/周五/周六。
国际会议名称
ConferenceⅠConference位于名称中间,有介词。
Group 1The 2009 International Conference on Information and Communications SecurityThe 2010 IEEE International Conference on Networking, Sensing and Control (ICNSC 2010)ICPR 2010 - The 20th International Conference on Pattern Recognition International Conference on Spoken Language Processing (ICSLP)(ICASSP ’98)ANZALS Leisure & Recreation Conference 2008: Leisure is the keyThe Eleventh IEEE International Conference on Computer Vision (ICCV 2007) The 18th International Conference on Pattern Recognition (ICPR 06)The 7th International Conference on Functional GrammarThe 5th International Conference on Advanced Materials and ProcessingThe 6th International Conference on Signal Processing Proceedings 2002The 8th Biennial Conference on Tourism in AsiaTourism, Hospitality & Foodservice Industry2010 The 2nd IEEE International Conference on Information Management and EngineeringGroup 2International Conference on Infrared and Millimeter WavesInternational Conference on Molecular Beam EpitaxyInternational Conference on Narrow Gap SemiconductorsInternational Conference on Thin Film Physics and ApplicationInternational Conference on X-Ray LasersInternational Conference on Solid-state and Integrated Circuit Technology ProceedingsInternational Conference on High Performance Computer Architecture (HPCA) International Conference on Communications (ICC)International Joint Conference on Artificial Intelligence (IJCAI)International Conference on Computer Vision (ICCV)International Conference on Pattern Recognition (ICPR)International Conference on Pattern Recognition (ICPR)International Conference on Robotics and AutomationInternational Conference on Intelligent Robots and SystemsInternational Conference on Software EngineeringInternational Conference on Efficiency, Costs, Optimization, Simulation and Environmental Impact of Energy SystemsInternational Conference on Turbochargers and TurbochargingInternational Conference on Electrical MachinesInternational Technical Conference on the Enhanced Safety of Vehicles(EVS)International Conference on Magnet TechnologyInternational Conference on Engineering and Technological Sciences 2000 (PITTCON), USA.International Conference on Fluidization (ICF)International Conference on Bioseparation Engineering (ICBE)International Conference on Molecular Beam EpitaxyInternational Conference on ELT in ChinaInternational Conference on Pragmatics and Language LearningInternational Conference on MagnetismInternational conference on High Pressure Semiconductor PhysicsInternational Conference on Ternary and Multlnary CompoundsInternational Conference on Shellfish RestorationInternational Conference on Industrial Engineering and Engineering ManagementInternational Conference on Information SystemThe International Conference on MOVPEWorld Conference on WomenWorld Conference on Photovoltaic Solar Energy ConversionGroup 3IEEE International Conference on Accoustic, Speech and Signal Processing TechnologyIEEE International Conference on Accoustic, Speech and Signal Processing IFAC International Conference on Automatic ControlIEEE Conference on Decision and ControlThe IEEE International Conference on Vehicular Electronics and Safety (ICVES)The International IEEE Conference on Intelligent Transportation Systems (ITSC)IUMRS International Conference on Advanced MaterialsAnnual JALT International Conference on Language Teaching and Learning Asian Conference on Computer Vision (ACCV)Asian Conference on High Pressure ResearchThe Pacific Rim Conference on Lasers and Electro-Optics (CLEO/PR)Pan-Asian Conference on Language Teaching (PAC)Portland International Conference on Management of Engineering and TechnologyThe Pittsburgh Conference on Analytical Chemistry and Applied Spectroscopy Group 4The 4th World Conference for Graduate Research in Tourism, Hospitality and International Conference for Optics(光学)World Conference of Animal Production (WCAP)The 2nd International Conference of Bionic EngineeringⅡConference位于句首,有介词。
计算机科学重要国际会议
1.2计算机科学与技术重要国际学术会议一、A类会议二、B类会议1.3自动化重要国际学术会议一、A类会议二、B类会议数据挖掘相关的权威期刊和会议-----------------------------------------------[Journals]1.ACM Transactions on Knowledge Discovery from Data (TKDD)2.IEEE Transactions on Knowledge and Data Engineering (TKDE)3.Data Mining and Knowledge Discovery4.Knowledge and Information Systems5.Data & Knowledge Engineering[Conferences]1.SIGMOD:ACM Conference on Management of Data (ACM)2.VLDB:International Conference on Very Large Data Bases (Morgan Kaufmann/ACM)3.ICDE:IEEE International Conference on Data Engineering (IEEE Computer Society)4.SIGKDD:ACM Knowledge Discovery and Data Mining (ACM)5.WWW:International World Wide Web Conferences (W3C)6.CIKM:ACM International Conference on Information and Knowledge Management (ACM)7.PKDD:European Conference on Principles and Practice of Knowledge Discovery in Databases (Springer-Verlag LNAI)JournalsACM TKDD /DMKD/content/1573-756X/?p=859c3e83455d41679ef1be783 e923d1d&pi=0IEEE TKDE /organizations/pubs/transactions/tkde.htm ACM TODS /tods/VLDB Journal /ACM Tois /pubs/tois/ConferencesSigKDD /ICDM /~icdm/SDM /meetings/sdm08/PKDD /VLDB /SigMod /sigmod/ICDE http://www.ipsi.fraunhofer.de/tcde/conf_e.htmlWWW /conferencesOnline Resources网址集合/Computers/Software/Databases/Data_Mining// A google co-op search engine for Data Mining/coop/cse?cx=006422944775554126616%3Aixcd3tdxkke Data Mining, University of Houston/boetticher/CSCI5931%20Data%20Mining.htmlData Mining Program, University of Central Florida / Data Mining Group, University of Dortmundhttp://www-ai.cs.uni-dortmund.de/index.htmlData Mining, MIT OCW/OcwWeb/Sloan-School-of-Management/15-062Data-MiningSpri ng2003/CourseHome/Data Mining Group, Tsinghua /dmg.html KDD oral presentations video Data Mining Events Feed /DataMiningEvents ToolsWeka /ml/weka/Rapid Miner(Yale) /content/view/3/76/lang,en/IlliMine /Alpha Miner http://www.eti.hku.hk/alphaminerPotter's Wheel A-B-C /abc/。
国际象棋比赛的英语作文
The world of chess is a realm of strategy, intellect, and intense competition. It is a game that has been played for centuries, with its roots stretching back to the 6th century in India. Over time, it has evolved into a sophisticated sport that tests the mental fortitude of its players. One of the most exhilarating aspects of chess is the international competitions that bring together the best minds from around the globe.In the heart of a bustling city, the air was thick with anticipation as the international chess tournament commenced. The venue was a grand hall, adorned with the flags of participating nations, each fluttering proudly in the gentle breeze that wafted through the open windows. The atmosphere was electric, abuzz with the chatter of excited spectators, the rustle of anxious players, and the occasional click of a chess piece being moved with calculated precision.The competitors were a diverse group, hailing from different corners of the world. There were seasoned veterans with years of experience, their faces etched with the wisdom of countless games played and won. Young prodigies, brimming with talent and eager to prove themselves, sat alongside them. Each player, regardless of age or experience, shared a common passion for the game that transcended language and culture.The games were a spectacle to behold. The chessboard, a battlefield of 64 squares, was where the real war took place. Each move was a strategic maneuver, a silent conversation between the players. The pieces, the pawns, knights, bishops, rooks, and queens, were the soldiers in this cerebral duel. The king, the ultimate target, was the symbol of victory ordefeat.The players sat hunched over their boards, their faces a mask of concentration. Some would furrow their brows in deep thought, while others would tap their fingers impatiently, waiting for their opponents move. The silence was occasionally broken by the soft thud of a piece being placed on the board, a sound that echoed through the hall, signaling a decisive move.The games were not just a test of skill but also of character. The players had to maintain their composure under pressure, to think several moves ahead, and to adapt to the everchanging dynamics of the game. It was a dance of wits, a silent duel that required patience, foresight, and an unwavering focus.The tournament was a celebration of the human minds capacity for strategic thinking and problemsolving. It showcased the beauty of chess as an art form, a blend of creativity and logic. The players were not just competitors they were artists, each painting their own unique masterpiece on the canvas of the chessboard.As the tournament progressed, the excitement only grew. The initial rounds whittled down the field, with each game bringing the players closer to the coveted title. The crowd watched with bated breath as the tension mounted, the air thick with the scent of victory and the sting of defeat.The final game was a nailbiting affair, a clash of titans that had theaudience on the edge of their seats. The two finalists, both seasoned players with a reputation for their tactical prowess, engaged in a fierce battle of wits. The game was a rollercoaster of emotions, with moments of brilliance and heartstopping blunders.In the end, it was a single move, a masterstroke that sealed the fate of the game. The champion emerged, a look of triumph on their face, while the runnerup graciously accepted their defeat. The applause was deafening, a testament to the respect and admiration the audience had for both players.The international chess tournament was more than just a competition it was a gathering of minds, a celebration of the game that brought people together from all walks of life. It was a testament to the power of chess as a universal language, a game that transcended borders and united people in their shared love for the sport.As the tournament concluded, the players and spectators alike left with a sense of fulfillment and a newfound appreciation for the game. The memories of the games played, the strategies employed, and the friendships forged would linger long after the last piece had been moved. The international chess competition was a reminder of the beauty of the human mind and the endless possibilities it holds when challenged.。
pattern classification书
Pattern Classification书是一本由Richard O. Duda、Peter E. Hart和David G. Stork合著的著名教科书,已经成为模式识别领域的经典教材。
本书自第一版出版以来,已经在机器学习、模式识别、人工智能等领域产生了深远的影响,被广泛地应用于学术研究和工程实践中。
本书内容丰富,深入浅出,涵盖了模式识别领域的基本理论、经典方法和最新进展。
以下是Pattern Classification书籍的主要内容:一、基本概念1. 模式识别的概念和任务模式识别是指根据已知的样本数据,通过建立模型和算法来进行分类、识别和预测未知数据的方法和技术。
本章介绍了模式识别的基本概念、任务和应用领域,为后续内容的学习打下基础。
2. 概率论与统计学基础概率论和统计学是模式识别领域的重要基础,本章介绍了概率论和统计学的基本原理和方法,包括概率分布、随机变量、统计推断等内容,为后续的分类器设计和性能评估提供了数学基础。
二、监督学习3. 最近邻法最近邻法是一种简单而有效的分类方法,本章介绍了最近邻法的原理、算法和应用,包括最近邻分类器的设计和性能分析,以及最近邻法在实际问题中的应用案例。
4. 线性判别分析线性判别分析是一种经典的监督学习方法,本章介绍了线性判别分析的原理、模型和求解方法,包括Fisher判别准则、最小均方误差准则等内容,为读者深入理解监督学习提供了重要参考。
5. 支持向量机支持向量机是一种强大的分类器,本章介绍了支持向量机的原理、核方法和参数选择,包括线性支持向量机、非线性支持向量机等内容,为读者掌握高效分类器提供了重要参考。
三、无监督学习6. 聚类分析聚类分析是一种无监督学习方法,本章介绍了聚类分析的原理、算法和应用,包括K均值聚类、层次聚类、密度聚类等内容,为读者理解无监督学习提供了重要帮助。
7. 主成分分析主成分分析是一种常用的降维方法,本章介绍了主成分分析的原理、模型和求解方法,包括特征值分解、奇异值分解等内容,为读者掌握数据压缩和特征提取技术提供了重要参考。
recognition
RecognitionRecognition is the process of acknowledging or identifying something or someone. It plays a vital role in various aspects of our lives, including communication, security, and machine learning. In this document, we will discuss different types of recognition, their applications, and the technologies behind them.Facial RecognitionFacial recognition is a biometric technology that identifies or verifies individuals by analyzing their facial features. It has gained significant popularity and application in rec ent years. Facial recognition systems capture an image or video of a person’s face and analyze unique facial landmarks, such as the size and shape of the eyes, nose, and mouth.Applications of facial recognition are diverse. One of the most common uses is in security systems, where it can be used to grant or deny access to restricted areas based on facial recognition. It is also used in mobile devices and social media platforms for user identification and authentication.Facial recognition technology has been the subject of debate due to privacy concerns. Organizations and governments need to ensure that the data collected through facial recognition systems is properly secured and used within legal boundaries.Speech RecognitionSpeech recognition is a technology that converts spoken language into written text. It enables interaction between humans and machines through voice commands. This technology has improved significantly in recent years, especially with the development of deep learning algorithms.The applications of speech recognition are widespread. Virtual assistants like Siri, Alexa, and Google Assistant rely on this technology to understand and respond to user commands. It is also used in transcription services, where audio files are automatically converted into text, saving time and effort.Speech recognition technology has also found applications in healthcare, where it is used to transcribe medical records, facilitate communication with patients with speech impairments, and assist in language translation.Object RecognitionObject recognition is the process of identifying and classifying objects or entities within an image or video. It involves extracting meaningful information from visual input and mapping it to known objects or categories.One of the key applications of object recognition is in autonomous driving. Self-driving cars utilize object recognition to identify and track other vehicles, pedestrians, traffic signs, and obstacles to navigate safely on the road.Object recognition is also used in the field of augmented reality, where virtual objects are overlaid onto the real world. This technology enables various interactive experiences, such as gaming, visualization, and shopping.Pattern RecognitionPattern recognition is a branch of machine learning that focuses on the automatic discovery of regularities or patterns within data. It involves the extraction of features from input data and the use of algorithms to identify similarities or anomalies.Pattern recognition has a wide range of applications in diverse fields. In finance, it is used to predict stock market trends or detect fraudulent activities. In healthcare, it helps in the diagnosis of diseases based on symptoms or medical images. It is also widely used in image and speech recognition systems.With the advancement of deep learning algorithms and the availability of massive amounts of data, pattern recognition has become an essential tool for data analysis and decision-making processes.ConclusionRecognition technologies have revolutionized various aspects of our lives. Whether it is facial recognition for security, speech recognition for virtual assistants, object recognition for autonomous driving, or pattern recognition for data analysis, these technologies have made our lives more convenient and efficient.However, it is important to address the ethical and privacy concerns associated with these technologies. Stringent regulations and safeguards should be in place to protect individuals’ rights and ensure responsible use of recog nition systems.In conclusion, recognition technologies continue to evolve, and we can expect even more innovative and impactful applications in the future. As technology advances, it is crucial to strike a balance between the benefits and risks associated with recognition systems.。
项目管理专用词汇缩写对照_10年亲身积累
FE FFC FFST FG FGT FIN FMEA FMS FOC FOT FOT FPN FPT FRC FRS FRU FSP FST FT FTZ FVT FVT GA GA GC GCM GM GPS GSC GTR GWS HC HCL HCS HDCP HDD HMM IAL ICAL IEC I/E IFU I/N I/O IBM ID IE
CMS CNC CoC CoC COO CP CPC CPCN CPD CPK CPM CPT CPT CPU ห้องสมุดไป่ตู้RC CRS CSA CSA CECC CSC CSP CSP CSRT CTN CTO CTQ DAU DAU DBCS DCP DCR DCR DCS DDC /CI DDP DE DE DEV DFC DFE DFU DFX DMADV DMAIC DMT DOC DOS
Full Name Advanced Access Content System Automatic Bright Limiting Agency for Electronic Communications Assist Management Chile, Ecuador, Peru, Bolivia, Venezuela Architecture Of Data Asia Pacific All Parts In Application Programming Interface after point of sales Argentina, Paraguay, Uruguay American Standard Code for Information Interchange Association of SouthEast Asian Nations Approved Vendor List Architecture Verification Test Assembly Verification Test AirWay Bill British Approval Board for Telecommunications Building Block Building Block Functional Validation Building Block Completeness Bulletin Board System Building Block Sponsor Evaluation Building Block User Evaluation Building Block Usage Evaluation brominated flame retardants Basic-Input-Output System Basic Manufacturing Cost Brand Management Team Bill of Materials BarCode Cross Brand Configuration Center Customs Clearance China Commodity Inspection Bureau Critical Components Management Compact disk Center for devices and radiological health (FDA) Commercial DeskTop Color Display Tube Central Europe Middle East/Africa Consumer Experience Specification Customer Fulfilment Computer Graphics Corporation Instruction Color, Material, Finish Consumer Marketing Management
传说中编程界的龙书、虎书、鲸书、魔法书……指的都是哪些?
传说中编程界的龙书、虎书、鲸书、魔法书……指的都是哪些?编译原理三⼤圣书(前3个)
1. 《编译原理》(龙书)
2. 《现代编译原理:C语⾔描述》(虎书)
3. 《⾼级编译器设计与实现》(鲸书)
4. 《编译器设计》(象书)
5. 《OpenGL编程指南(第⼋版)》 (红宝书)
6. 《OpenGL超级宝典》(蓝宝书)
7. 《OpenGL着⾊语⾔》(橙宝书)
8. 《DirectX 9.0 3D游戏开发编程基础》(红龙书)
9. 《计算机程序的构造和解释》魔法书
10. 《Java⾼级程序设计》(红宝书)
11. 《Java权威指南》(犀⽜书)
12. 《Java语⾔精粹》(蝴蝶书)
13. 《编写可维护的Java》(乌龟书)
14. 《Java Web 富应⽤开发》(猫头鹰书)
O`Reily 出版了许多动物书,书脊颜⾊还挺好看的。
列出⽬前 O’Reilly 全部书籍的书名、封⾯颜⾊和动物名称,如果你想了解哪本书的封⾯,可以⾄此查阅
有些书的称号则是来根据作者命名的
1. 《算法导论》(CLRS )
2. 《设计模式》(GOF)
3. 《C程序设计语⾔》( K&R)
根据书名的⾸个字母命名的
1. 根据书名的⾸个字母命名的
2. 《计算机程序设计艺术》(TAOCP)
补充:
《机器学习》周志华(西⽠书)
《深度学习》(花书)
《模式识别与机器学习》Pattern Recognition and Machine Learning (PRML)
Machine Learning:A Probabilistic Prospective (MLaPP )。
模式识别文献综述
模式识别文献综述摘要自20世纪60年代以来,模式识别的理论与方法研究及在工程中的实际应用取得了很大的进展。
本文先简要回顾模式识别领域的发展历史和主要方法的演变,然后围绕模式分类这个模式识别的核心问题,就概率密度估计、特征选择和变换、分类器设计几个方面介绍近年来理论和方法研究的主要进展,最后简要分析将来的发展趋势。
1. 前言模式识别(Pattern Recognition)是对感知信号(图像、视频、声音等)进行分析,对其中的物体对象或行为进行判别和解释的过程。
模式识别能力普遍存在于人和动物的认知系统,是人和动物获取外部环境知识,并与环境进行交互的重要基础。
我们现在所说的模式识别一般是指用机器实现模式识别过程,是人工智能领域的一个重要分支。
早期的模式识别研究是与人工智能和机器学习密不可分的,如 Rosenblatt 的感知机[1]和 Nilsson的学习机[2]就与这三个领域密切相关。
后来,由于人工智能更关心符号信息和知识的推理,而模式识别更关心感知信息的处理,二者逐渐分离形成了不同的研究领域。
介于模式识别和人工智能之间的机器学习在 20 世纪 80 年代以前也偏重于符号学习,后来人工神经网络重新受到重视,统计学习逐渐成为主流,与模式识别中的学习问题渐趋重合,重新拉近了模式识别与人工智能的距离。
模式识别与机器学习的方法也被广泛用于感知信号以外的数据分析问题(如文本分析、商业数据分析、基因表达数据分析等),形成了数据挖掘领域。
模式分类是模式识别的主要任务和核心研究内容。
分类器设计是在训练样本集合上进行优化(如使每一类样本的表达误差最小或使不同类别样本的分类误差最小)的过程,也就是一个机器学习过程。
由于模式识别的对象是存在于感知信号中的物体和现象,它研究的内容还包括信号/图像/视频的处理、分割、形状和运动分析等,以及面向应用(如文字识别、语音识别、生物认证、医学图像分析、遥感图像分析等)的方法和系统研究。
Face Recognition A Literature Review
Abstract—The task of face recognition has been actively researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented. Description and limitations of face databases which are used to test the performance of these face recognition algorithms are given. A brief summary of the face recognition vendor test (FRVT) 2002, a large scale evaluation of automatic face recognition technology, and its conclusions are also given. Finally, we give a summary of the research results.Keywords—Combined classifiers, face recognition, graph matching, neural networks.I.I NTRODUCTIONACE recognition is an important research problem spanning numerous fields and disciplines. This because face recognition, in additional to having numerous practical applications such as bankcard identification, access control, Mug shots searching, security monitoring, and surveillance system, is a fundamental human behaviour that is essential for effective communications and interactions among people.A formal method of classifying faces was first proposed in[1]. The author proposed collecting facial profiles as curves, finding their norm, and then classifying other profiles by their deviations from the norm. This classification is multi-modal, i.e. resulting in a vector of independent measures that could be compared with other vectors in a database.Progress has advanced to the point that face recognition systems are being demonstrated in real-world settings [2]. The rapid development of face recognition is due to a combination of factors: active development of algorithms, the availability of a large databases of facial images, and a method for evaluating the performance of face recognition algorithms.In the literatures, face recognition problem can be formulated as: given static (still) or video images of a scene, identify or verify one or more persons in the scene by comparing with faces stored in a database.When comparing person verification to face recognition, there are several aspects which differ. First, a client – an authorized user of a personal identification system – is Manuscript received February 22, 2005.A. S. Tolba is with the Information Systems Department, Mansoura University, Egypt, (e-mail: tolba1954@)).A. H. EL-Baz is with the Mathematics Department, Damietta Faculty of Science, New Damietta, Egypt, and doing PhD research on pattern recognition (phone: 0020-57-403980; Fax: 0020-57–403868; e-mail: ali_elbaz@).A. H. EL-Harby is with the Mathematics Department, Damietta Faculty of Science, New Damietta, Egypt, (e-mail: elharby@). assumed to be co-operative and makes an identity claim. Computationally this means that it is not necessary to consult the complete set of database images (denoted model images below) in order to verify a claim. An incoming image (referred to as a probe image) is thus compared to a small number of model images of the person whose identity is claimed and not, as in the recognition scenario, with every image (or some descriptor of an image) in a potentially large database. Second, an automatic authentication system must operate in near-real time to be acceptable to users. Finally, in recognition experiments, only images of people from the training database are presented to the system, whereas the case of an imposter (most likely a previously unseen person) is of utmost importance for authentication.Face recognition is a biometric approach that employs automated methods to verify or recognize the identity of a living person based on his/her physiological characteristics. In general, a biometric identification system makes use of either physiological characteristics (such as a fingerprint, iris pattern, or face) or behaviour patterns (such as hand-writing, voice, or key-stroke pattern) to identify a person. Because of human inherent protectiveness of his/her eyes, some people are reluctant to use eye identification systems. Face recognition has the benefit of being a passive, non intrusive system to verify personal identity in a “natural” and friendly way.In general, biometric devices can be explained with a three-step procedure (1) a sensor takes an observation. The type of sensor and its observation depend on the type of biometric devices used. This observation gives us a “Biometric Signature” of the individual. (2) a computer algorithm “normalizes” the biometric signature so that it is in the same format (size, resolution, view, etc.) as the signatures on the system’s database. The normalization of the biometric signature gives us a “Normalized Signature” of the individual.(3) a matcher compares the normalized signature with the set (or sub-set) of normalized signatures on the system's database and provides a “similarity score” that compares the individual's normalized signature with each signature in the database set (or sub-set). What is then done with the similarity scores depends on the biometric system’s application?Face recognition starts with the detection of face patterns in sometimes cluttered scenes, proceeds by normalizing the face images to account for geometrical and illumination changes, possibly using information about the location and appearance of facial landmarks, identifies the faces using appropriate classification algorithms, and post processes the results using model-based schemes and logistic feedback [3].The application of face recognition technique can be categorized into two main parts: law enforcement application and commercial application. Face recognition technology isFace Recognition: A Literature ReviewA. S. Tolba, A.H. El-Baz, and A.A. El-HarbyFprimarily used in law enforcement applications, especially Mug shot albums (static matching) and video surveillance (real-time matching by video image sequences). The commercial applications range from static matching of photographs on credit cards, ATM cards, passports, driver’s licenses, and photo ID to real-time matching with still images or video image sequences for access control. Each application presents different constraints in terms of processing.All face recognition algorithms consistent of two major parts: (1) face detection and normalization and (2) face identification. Algorithms that consist of both parts are referred to as fully automatic algorithms and those that consist of only the second part are called partially automatic algorithms. Partially automatic algorithms are given a facial image and the coordinates of the center of the eyes. Fully automatic algorithms are only given facial images. On the other hand, the development of face recognition over the past years allows an organization into three types of recognition algorithms, namely frontal, profile, and view-tolerant recognition, depending on the kind of images and the recognition algorithms. While frontal recognition certainly is the classical approach, view-tolerant algorithms usually perform recognition in a more sophisticated fashion by taking into consideration some of the underlying physics, geometry, and statistics. Profile schemes as stand-alone systems have a rather marginal significance for identification, (for more detail see [4]). However, they are very practical either for fast coarse pre-searches of large face database to reduce the computational load for a subsequent sophisticated algorithm, or as part of a hybrid recognition scheme. Such hybrid approaches have a special status among face recognition systems as they combine different recognition approaches in an either serial or parallel order to overcome the shortcoming of the individual components.Another way to categorize face recognition techniques is to consider whether they are based on models or exemplars. Models are used in [5] to compute the Quotient Image, and in [6] to derive their Active Appearance Model. These models capture class information (the class face), and provide strong constraints when dealing with appearance variation. At the other extreme, exemplars may also be used for recognition. The ARENA method in [7] simply stores all training and matches each one against the task image. As far we can tell, current methods that employ models do not use exemplars, and vice versa. This is because these two approaches are by no means mutually exclusive. Recently, [8] proposed a way of combining models and exemplars for face recognition. In which, models are used to synthesize additional training images, which can then be used as exemplars in the learning stage of a face recognition system.Focusing on the aspect of pose invariance, face recognition approaches may be divided into two categories: (i) global approach and (ii) component-based approach. In global approach, a single feature vector that represents the whole face image is used as input to a classifier. Several classifiers have been proposed in the literature e.g. minimum distance classification in the eigenspace [9,10], Fisher’s discriminant analysis [11], and neural networks [12]. Global techniques work well for classifying frontal views of faces. However, they are not robust against pose changes since global features are highly sensitive to translation and rotation of the face. To avoid this problem an alignment stage can be added before classifying the face. Aligning an input face image with a reference face image requires computing correspondence between the two face images. The correspondence is usually determined for a small number of prominent points in the face like the center of the eye, the nostrils, or the corners of the mouth. Based on these correspondences, the input face image can be warped to a reference face image.In [13], an affine transformation is computed to perform the warping. Active shape models are used in [14] to align input faces with model faces. A semi-automatic alignment step in combination with support vector machines classification was proposed in [15]. An alternative to the global approach is to classify local facial components. The main idea of component based recognition is to compensate for pose changes by allowing a flexible geometrical relation between the components in the classification stage.In [16], face recognition was performed by independently matching templates of three facial regions (eyes, nose and mouth). The configuration of the components during classification was unconstrained since the system did not include a geometrical model of the face. A similar approach with an additional alignment stage was proposed in [17]. In [18], a geometrical model of a face was implemented by a 2D elastic graph. The recognition was based on wavelet coefficients that were computed on the nodes of the elastic graph. In [19], a window was shifted over the face image and the DCT coefficients computed within the window were fed into a 2D Hidden Markov Model.Face recognition research still face challenge in some specific domains such as pose and illumination changes. Although numerous methods have been proposed to solve such problems and have demonstrated significant promise, the difficulties still remain. For these reasons, the matching performance in current automatic face recognition is relatively poor compared to that achieved in fingerprint and iris matching, yet it may be the only available measuring tool for an application. Error rates of 2-25% are typical. It is effective if combined with other biometric measurements.Current systems work very well whenever the test image to be recognized is captured under conditions similar to those of the training images. However, they are not robust enough if there is variation between test and training images [20]. Changes in incident illumination, head pose, facial expression, hairstyle (include facial hair), cosmetics (including eyewear) and age, all confound the best systems today.As a general rule, we may categorize approaches used to cope with variation in appearance into three kinds: invariant features, canonical forms, and variation- modeling. The first approach seeks to utilize features that are invariant to the changes being studied. For instance, the Quotient Image [5] is (by construction) invariant to illumination and may be used to recognize faces (assumed to be Lambertian) when lighting conditions change.The second approach attempts to “normalize” away the variation, either by clever image transformations or by synthesizing a new image (from the given test image) in some“canonical” or “prototypical” form. Recognition is then performed using this canonical form. Examples of this approach include [21,22]. In [21], for instance, the test image under arbitrary illumination is re-rendered under frontal illumination, and then compared against other frontally-illuminated prototypes.The third approach of variation-modeling is self explanatory: the idea is to learn, in some suitable subspace, the extent of the variation in that space. This usually leads to some parameterization of the subspace(s). Recognition is then performed by choosing the subspace closest to the test image, after the latter has been appropriately mapped. In effect, the recognition step recovers the variation (e.g. pose estimation) as well as the identity of the person. For examples of this technique, see [18, 23, 24 and 25].Despite the plethora of techniques, and the valiant effort of many researchers, face recognition remains a difficult, unsolved problem in general. While each of the above approaches works well for the specific variation being studied, performance degrades rapidly when other variations are present. For instance, a feature invariant to illumination works well as long as pose or facial expression remains constant, but fails to be invariant when pose or expression is changed. This is not a problem for some applications, such as controlling access to a secured room, since both the training and test images may be captured under similar conditions. However, for general, unconstrained recognition, none of these techniques are robust enough.Moreover, it is not clear that different techniques can be combined to overcome each other’s limitations. Some techniques, by their very nature, exclude others. For example, the Symmetric Shape-from-Shading method of [22] relies on the approximate symmetry of a frontal face. It is unclear how this may be combined with a technique that depends on side profiles, where the symmetry is absent.We can make two important observations after surveying the research literature: (1) there does not appear to be any feature, set of features, or subspace that is simultaneously invariant to all the variations that a face image may exhibit, (2) given more training images, almost any technique will perform better. These two factors are the major reasons why face recognition is not widely used in real-world applications. The fact is that for many applications, it is usual to require the ability to recognize faces under different variations, even when training images are severely limited.II.L ITERATURE R EVIEW OF F ACE R ECOGNITION T ECHNIQUES This section gives an overview on the major human face recognition techniques that apply mostly to frontal faces, advantages and disadvantages of each method are also given. The methods considered are eigenfaces (eigenfeatures), neural networks, dynamic link architecture, hidden Markov model, geometrical feature matching, and template matching. The approaches are analyzed in terms of the facial representations they used.A.EigenfacesEigenface is one of the most thoroughly investigated approaches to face recognition. It is also known as Karhunen- Loève expansion, eigenpicture, eigenvector, and principal component. References [26, 27] used principal component analysis to efficiently represent pictures of faces. They argued that any face images could be approximately reconstructed by a small collection of weights for each face and a standard face picture (eigenpicture). The weights describing each face are obtained by projecting the face image onto the eigenpicture. Reference [28] used eigenfaces, which was motivated by the technique of Kirby and Sirovich, for face detection and identification.In mathematical terms, eigenfaces are the principal components of the distribution of faces, or the eigenvectors of the covariance matrix of the set of face images. The eigenvectors are ordered to represent different amounts of the variation, respectively, among the faces. Each face can be represented exactly by a linear combination of the eigenfaces. It can also be approximated using only the “best” eigenvectors with the largest eigenvalues. The best M eigenfaces construct an M dimensional space, i.e., the “face space”. The authors reported 96 percent, 85 percent, and 64 percent correct classifications averaged over lighting, orientation, and size variations, respectively. Their database contained 2,500 images of 16 individuals.As the images include a large quantity of background area, the above results are influenced by background. The authors explained the robust performance of the system under different lighting conditions by significant correlation between images with changes in illumination. However, [29] showed that the correlation between images of the whole faces is not efficient for satisfactory recognition performance. Illumination normalization [27] is usually necessary for the eigenfaces approach.Reference [30] proposed a new method to compute the covariance matrix using three images each was taken in different lighting conditions to account for arbitrary illumination effects, if the object is Lambertian. Reference [31] extended their early work on eigenface to eigenfeatures corresponding to face components, such as eyes, nose, and mouth. They used a modular eigenspace which was composed of the above eigenfeatures (i.e., eigeneyes, eigennose, and eigenmouth). This method would be less sensitive to appearance changes than the standard eigenface method. The system achieved a recognition rate of 95 percent on the FERET database of 7,562 images of approximately 3,000 individuals. In summary, eigenface appears as a fast, simple, and practical method. However, in general, it does not provide invariance over changes in scale and lighting conditions. Recently, in [32] experiments with ear and face recognition, using the standard principal component analysis approach , showed that the recognition performance is essentially identical using ear images or face images and combining the two for multimodal recognition results in a statistically significant performance improvement. For example, the difference in the rank-one recognition rate for the day variation experiment using the 197-image training sets is90.9% for the multimodal biometric versus 71.6% for the ear and 70.5% for the face.There is substantial related work in multimodal biometrics. For example [33] used face and fingerprint in multimodal biometric identification, and [34] used face and voice. However, use of the face and ear in combination seems more relevant to surveillance applications.B.Neural NetworksThe attractiveness of using neural networks could be due to its non linearity in the network. Hence, the feature extraction step may be more efficient than the linear Karhunen-Loève methods. One of the first artificial neural networks (ANN) techniques used for face recognition is a single layer adaptive network called WISARD which contains a separate network for each stored individual [35]. The way in constructing a neural network structure is crucial for successful recognition. It is very much dependent on the intended application. For face detection, multilayer perceptron [36] and convolutional neural network [37] have been applied. For face verification, [38] is a multi-resolution pyramid structure. Reference [37] proposed a hybrid neural network which combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimension reduction and invariance to minor changes in the image sample. The convolutional network extracts successively larger features in a hierarchical set of layers and provides partial invariance to translation, rotation, scale, and deformation. The authors reported 96.2% correct recognition on ORL database of 400 images of 40 individuals.The classification time is less than 0.5 second, but the training time is as long as 4 hours. Reference [39] used probabilistic decision-based neural network (PDBNN) which inherited the modular structure from its predecessor, a decision based neural network (DBNN) [40]. The PDBNN can be applied effectively to 1) face detector: which finds the location of a human face in a cluttered image, 2) eye localizer: which determines the positions of both eyes in order to generate meaningful feature vectors, and 3) face recognizer. PDNN does not have a fully connected network topology. Instead, it divides the network into K subnets. Each subset is dedicated to recognize one person in the database. PDNN uses the Guassian activation function for its neurons, and the output of each “face subnet” is the weighted summation of the neuron outputs. In other words, the face subnet estimates the likelihood density using the popular mixture-of-Guassian model. Compared to the AWGN scheme, mixture of Guassian provides a much more flexible and complex model for approximating the time likelihood densities in the face space. The learning scheme of the PDNN consists of two phases, in the first phase; each subnet is trained by its own face images. In the second phase, called the decision-based learning, the subnet parameters may be trained by some particular samples from other face classes. The decision-based learning scheme does not use all the training samples for the training. Only misclassified patterns are used. If the sample is misclassified to the wrong subnet, the rightful subnet will tune its parameters so that its decision-region can be moved closer to the misclassified sample.PDBNN-based biometric identification system has the merits of both neural networks and statistical approaches, and its distributed computing principle is relatively easy to implement on parallel computer. In [39], it was reported that PDBNN face recognizer had the capability of recognizing up to 200 people and could achieve up to 96% correct recognition rate in approximately 1 second. However, when the number of persons increases, the computing expense will become more demanding. In general, neural network approaches encounter problems when the number of classes (i.e., individuals) increases. Moreover, they are not suitable for a single model image recognition test because multiple model images per person are necessary in order for training the systems to “optimal” parameter setting.C.Graph MatchingGraph matching is another approach to face recognition. Reference [41] presented a dynamic link structure for distortion invariant object recognition which employed elastic graph matching to find the closest stored graph. Dynamic link architecture is an extension to classical artificial neural networks. Memorized objects are represented by sparse graphs, whose vertices are labeled with a multiresolution description in terms of a local power spectrum and whose edges are labeled with geometrical distance vectors. Object recognition can be formulated as elastic graph matching which is performed by stochastic optimization of a matching cost function. They reported good results on a database of 87 people and a small set of office items comprising different expressions with a rotation of 15 degrees.The matching process is computationally expensive, taking about 25 seconds to compare with 87 stored objects on a parallel machine with 23 transputers. Reference [42] extended the technique and matched human faces against a gallery of 112 neutral frontal view faces. Probe images were distorted due to rotation in depth and changing facial expression. Encouraging results on faces with large rotation angles were obtained. They reported recognition rates of 86.5% and 66.4% for the matching tests of 111 faces of 15 degree rotation and 110 faces of 30 degree rotation to a gallery of 112 neutral frontal views. In general, dynamic link architecture is superior to other face recognition techniques in terms of rotation invariance; however, the matching process is computationally expensive.D.Hidden Markov Models (HMMs)Stochastic modeling of nonstationary vector time series based on (HMM) has been very successful for speech applications. Reference [43] applied this method to human face recognition. Faces were intuitively divided into regions such as the eyes, nose, mouth, etc., which can be associated with the states of a hidden Markov model. Since HMMs require a one-dimensional observation sequence and images are two-dimensional, the images should be converted into either 1D temporal sequences or 1D spatial sequences.In [44], a spatial observation sequence was extracted from a face image by using a band sampling technique. Each face image was represented by a 1D vector series of pixel observation. Each observation vector is a block of L lines and there is an M lines overlap between successive observations. An unknown test image is first sampled to an observation sequence. Then, it is matched against every HMMs in the model face database (each HMM represents a different subject). The match with the highest likelihood is considered the best match and the relevant model reveals the identity of the test face.The recognition rate of HMM approach is 87% using ORL database consisting of 400 images of 40 individuals. A pseudo 2D HMM [44] was reported to achieve a 95% recognition rate in their preliminary experiments. Its classification time and training time were not given (believed to be very expensive). The choice of parameters had been based on subjective intuition.E.Geometrical Feature MatchingGeometrical feature matching techniques are based on the computation of a set of geometrical features from the picture of a face. The fact that face recognition is possible even at coarse resolution as low as 8x6 pixels [45] when the single facial features are hardly revealed in detail, implies that the overall geometrical configuration of the face features is sufficient for recognition. The overall configuration can be described by a vector representing the position and size of the main facial features, such as eyes and eyebrows, nose, mouth, and the shape of face outline.One of the pioneering works on automated face recognition by using geometrical features was done by [46] in 1973. Their system achieved a peak performance of 75% recognition rate on a database of 20 people using two images per person, one as the model and the other as the test image. References [47,48] showed that a face recognition program provided with features extracted manually could perform recognition apparently with satisfactory results. Reference [49] automatically extracted a set of geometrical features from the picture of a face, such as nose width and length, mouth position, and chin shape. There were 35 features extracted form a 35 dimensional vector. The recognition was then performed with a Bayes classifier. They reported a recognition rate of 90% on a database of 47 people.Reference [50] introduced a mixture-distance technique which achieved 95% recognition rate on a query database of 685 individuals. Each face was represented by 30 manually extracted distances. Reference [51] used Gabor wavelet decomposition to detect feature points for each face image which greatly reduced the storage requirement for the database. Typically, 35-45 feature points per face were generated. The matching process utilized the information presented in a topological graphic representation of the feature points. After compensating for different centroid location, two cost values, the topological cost, and similarity cost, were evaluated. The recognition accuracy in terms of the best match to the right person was 86% and 94% of the correct person's faces was in the top three candidate matches.In summary, geometrical feature matching based on precisely measured distances between features may be most useful for finding possible matches in a large database such as a Mug shot album. However, it will be dependent on the accuracy of the feature location algorithms. Current automated face feature location algorithms do not provide a high degree of accuracy and require considerable computational time.F.Template MatchingA simple version of template matching is that a test image represented as a two-dimensional array of intensity values is compared using a suitable metric, such as the Euclidean distance, with a single template representing the whole face. There are several other more sophisticated versions of template matching on face recognition. One can use more than one face template from different viewpoints to represent an individual's face.A face from a single viewpoint can also be represented by a set of multiple distinctive smaller templates [49,52]. The face image of gray levels may also be properly processed before matching [53]. In [49], Bruneli and Poggio automatically selected a set of four features templates, i.e., the eyes, nose, mouth, and the whole face, for all of the available faces. They compared the performance of their geometrical matching algorithm and template matching algorithm on the same database of faces which contains 188 images of 47 individuals. The template matching was superior in recognition (100 percent recognition rate) to geometrical matching (90 percent recognition rate) and was also simpler. Since the principal components (also known as eigenfaces or eigenfeatures) are linear combinations of the templates in the data basis, the technique cannot achieve better results than correlation [49], but it may be less computationally expensive. One drawback of template matching is its computational complexity. Another problem lies in the description of these templates. Since the recognition system has to be tolerant to certain discrepancies between the template and the test image, this tolerance might average out the differences that make individual faces unique.In general, template-based approaches compared to feature matching are a more logical approach. In summary, no existing technique is free from limitations. Further efforts are required to improve the performances of face recognition techniques, especially in the wide range of environments encountered in real world.G.3D Morphable ModelThe morphable face model is based on a vector space representation of faces [54] that is constructed such that any convex combination of shape and texture vectors of a set of examples describes a realistic human face.Fitting the 3D morphable model to images can be used in two ways for recognition across different viewing conditions: Paradigm 1. After fitting the model, recognition can be based on model coefficients, which represent intrinsic shape and texture of faces, and are independent of the imaging conditions: Paradigm 2. Three-dimension face reconstruction can also be employed to generate synthetic views from gallery probe images [55-58]. The synthetic views are then。
IPK Gatersleben, Pattern Recognition Group, Gatersleben,
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ESANN'2005 proceedings - European Symposium on Artificial Neural Networks Bruges (Belgium), 27-29 April 2005, d-side publi., ISBN 2-930307-05-6.
perception and self-ideal discrepancies can be detected [1]. The second questionnaire is the Freiburg Personality Inventory (FPI) reflecting a set of personality properties including social orientation, inhibition, contentment, aggressiveness, stress, somatic complaints, openess, sensitivity and emotionality/neuroticism [9]. The set of questionnaires is completed by the Invetory for Assesment of Interpersonal Relations (IIP) which judges the interpersonal relations. Based on these data, the question for machine learning is to identify the relevant attributes and a possible hypothesis how experts and BPI, respectively, make their decision. We tackle this problem learning classifiers from the given data, which allow insight into their classification and which provide a hypothesis for the respective decision rule.
“技术-教学-学科知识”(TPACK)研究:最新进展与趋向
“技术-教学-学科知识”(TPACK)研究:最新进展与趋向蔡敬新;邓峰【摘要】随着信息技术与课程整合的理念在教育领域不断渗入,教师如何在教学中有效使用信息技术成为教育研究者关注的热门话题之一。
近十年来,不少学者致力于“技术-教学-学科知识”(TPACK)这一理论框架的发展及其在教学实践中的应用研究,包括对TPACK概念探讨研究、七因子的结构测量与模型研究、TPACK个案研究、增强TPACK的策略研究以及TPACK的通适性与专属性研究等。
目前关于TPACK的理论观点主要包括三种:另类PCK观、融合促进性观和单一发展性观。
三种观点各有偏重,均有可取之处。
不少实证研究表明,七个TPACK因子在识别上具有一定困难和挑战性;一些其他因素,如教师是否接受培训,教师教学“情感-信念”等可能会影响TPACK七因子模型。
未来的TPACK研究,应该考虑其高度情境性以及在技术、教学及学科方面的专属性,在不同国家地区、不同学科领域(尤其是语言与艺术)、不同教学模式及不同技术载体的背景下测查教师的TPACK结构及其发展机理;同时也需要从“知识创建”与“设计能力”的角度,以及学生的角度来诠释与发展教师的TPACK。
%With the pervasion of the ICT integration in the field of education, how teachers effectively use ICT in their teaching becomes one of the hot topics among educational researchers. In the past decade, many scholars have been working on the development of the framework of technological pedagogical content knowledge (TPACK) and its application in teaching practice. These include theoretical discussion on TPACK, measurement of the seven TPACK components and their relationships, case studies on TPACK, strategies for enhancing TPACK, as well as the generality and specificity of TPACK. Therehave been three main perspectives of TPACK, namely alternative PCK view, integrative-facilitative view, and distinctive-developmental view, each of which has their own focus and merit. Most empirical studies indicate the difficulty and challenge of teasing apart the seven TPACK components. Potential factors include the training of teachers and teachers' affect/belief. Future TPACK research should take into account the highly contextual nature of TPACK and its specificity to technology, pedagogy, and content. More research can investigate how teachers' TPACK may develop within different countries/regions, subject matters (especially Language and Arts), instructional models, and technology-embedded environments. It is also necessary to interpret and develop teachers' TPACK from the lens of knowledge creation and design capacity, and that of students.【期刊名称】《现代远程教育研究》【年(卷),期】2015(000)003【总页数】10页(P9-18)【关键词】TPACK;理论派别;实证探索;研究进展;研究趋向【作者】蔡敬新;邓峰【作者单位】新加坡南洋理工大学新加坡637616;新加坡南洋理工大学新加坡637616【正文语种】中文【中图分类】G434□[新加坡]蔡敬新邓峰随着信息技术与课程整合的理念在教育领域不断渗入,教师如何在教学中有效地使用信息技术成为教育研究者关注的热门话题之一(Deng et al.,2011;Jimoyiannis,2010)。
(转载)计算机方向的一些顶级会议和期刊
(转载)计算机⽅向的⼀些顶级会议和期刊Computer VisionConf.:Best: ICCV, Inter. Conf. on Computer VisionCVPR, Inter. Conf. on Computer Vision and Pattern RecognitionGood: ECCV, Euro. Conf. on Comp. VisionICIP, Inter. Conf. on Image ProcessingICPR, Inter. Conf. on Pattern RecognitionACCV, Asia Conf. on Comp. VisionJour.:Best: PAMI, IEEE Trans. on Patt. Analysis and Machine IntelligenceIJCV, Inter. Jour. on Comp. VisionGood:CVIU, Computer Vision and Image UnderstandingPR, Pattern Reco.NetworkConf.:ACM/SigCOMM ACM Special Interest Group of Communication..ACM/SigMetric 这个系统⽅⾯也有不少的Info Com ⼏百⼈的⼤会,不如ACM/SIG的精。
Globe Com 这个就很⼀般了,不过有时候会有⼀些新的想法提出来。
Jour.:ToN (ACM/IEEE Transaction on Network)A.I.Conf.:AAAI: American Association for Artificial IntelligenceACM/SigIR: 这个是IR⽅⾯的,可能DB/AI的⼈都有IJCAI: International Joint Conference on Artificial IntelligenceNIPS: Neural Information Processing SystemsICML: International Conference on Machine LearningJour.:Machine LearningNEURAL COMPUTATION: 这个的影响因⼦在AI⾥最⾼,2000年为1.921ARTIFICIAL INTELLIGENCE: 1.683(2000年的数据,下同)PAMI: 1.668IEEE TRANSACTIONS ON FUZZY SYSTEMS: 1.597IEEE TRANSACTIONS ON NEURAL NETWORKS: 1.395AI MAGAZINE: 1.044NEURAL NETWORKS: 1.019PATTERN RECOGNITION: 0.781IMAGE AND VISION COMPUTING: 0.616IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING: 0.465APPLIED INTELLIGENCE: 0.268OS,SystemConf.:SOSP: The ACM Symposium on Operating Systems Principles(2年⼀次,想中⼀篇太难了)OSDI: USENIX Symposium on Operating Systems Design and ImplementationDatabaseConf.:ACM SIGMODVLDB:International Conference on Very Large Data BasesICDE:International Conference on Data Engineering//这三个会议并称为数据库⽅向的三⼤顶级会议SecurityConf.:IEEE Security and PrivacyCCS: ACM Computer and Communications SecurityNDSS (Network and Distributed Systems Security)WebConf.:WWW(International World Wide Web Conference)TheoryConf.:STOCFOCSEDAConf.:Best:DAC: IEEE/ACM Design Automation ConferenceICCAD: IEEE International Conference on Computer Aided DesignGood:ISCAS: IEEE International Symposium on Circuits And SystemsISPD: IEEE International Symposium on Physical DesignICCD: IEEE International Conference on Computer DesignASP-DAC: European Design Automation ConferenceE-DAC: Asia and South Pacific Design Automation Conference备注:x-DAC有很多,是地区性最⾼级DAC会议,上⾯两个影响最⼴。
四川大学模式识别Pattern Recognition教学大纲
College of Software EngineeringUndergraduate Course SyllabusCourse ID 311021020 Course Name Pattern RecognitionCourseAttribute□Compulsory ■Selective Course Language□English ■Chinese Credit Hour 2 Period32Semester□First Fall □First Spring □Second Fall □Second Spring□Third Fall ■Third Spring □Fourth Fall □Fourth Spring Instructors He KunDescription This course will mainly introduce the following knowledge to the students: (1)Bayes formula Decision Theory。
(2)Probability density function estimation。
(3)linear difference function。
(4)nonlinear difference function。
(5)neighbor method.。
(6)empirical risk minimization and orderly risk minimization method。
(7)Characteristics choose and extraction。
(8)K-L expansion based Feature Extraction。
(9)unsupervised studying method。
(10)Artificial Neural Network。
(11)Fuzzy Pattern Recognition method。
pattern短语
pattern短语"pattern" 是一个多义词,可以表示多种不同的概念和用法。
以下是一些与"pattern" 相关的常用短语:1. Follow a Pattern: 遵循某种模式或规律。
- Example: The weather seems to follow a pattern of hot days followed by thunderstorms.2. Pattern Recognition: 模式识别,指识别和理解事物的规律或模式。
- Example: Pattern recognition is an important skill in artificial intelligence.3. Set a Pattern: 设定一种常规或习惯。
- Example: By arriving late every day, he set a pattern of irresponsibility.4. Repeating Pattern: 重复的模式,指在一系列事件中出现的相同或类似的情况。
- Example: There is a repeating pattern of behavior in this group.5. Pattern of Behavior: 行为模式,指一个人或群体的行为的重复方式。
- Example: His pattern of behavior suggests a lack of commitment.6. Patterned Design: 有规律的设计,指具有重复或有规律的图案或装饰。
- Example: The curtains have a beautifully patterned design.7. Pattern Language: 模式语言,指描述问题和解决方案之间关系的方法。
- Example: Pattern languages are often used in architecture and software design.8. Pattern Recognition Software: 模式识别软件,用于自动识别和分类数据中的模式的计算机程序。
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Infrared polarization measurements and modeling applied to surface-laid antipersonnellandminesFrank Cremer,MEMBER SPIETNO Physics and Electronics Laboratory P.O.Box96864NL-2509JG,The HagueThe NetherlandsandDelft University of TechnologyPattern Recognition GroupSection of Applied GeophysicsDelft,The NetherlandsE-mail:cremer@fel.tno.nlWim de Jong,MEMBER SPIEKlamer SchutteTNO Physics and Electronics Laboratory P.O.Box96864NL-2509JG,The HagueThe Netherlands Abstract.Linear polarization of thermal infrared(TIR)radiation occurs when radiation is reflected or emitted from a smooth surface(such as the top of a landmine)and observed from a grazing angle.The background (soil and vegetation)is generally much rougher and therefore shows less pronounced linearly polarized radiation.This difference in polarization is utilized to enhance detection of landmines using TIR cameras.A setup has been constructed for the acquisition of polarized TIR images.This setup contains a polarizationfilter that rotates synchronously to the frame sync of the camera.Either a long wave infrared(LWIR)or a mid wave infrared(MWIR)camera can be mounted behind the rotating po-larizationfilter.The synchronization allows a sequence of images to be taken with a predefined constant angle of rotation between the images. Using this image sequence,three independent Stokes images are cal-culated,consisting of the unpolarized radiance,the difference between vertically and horizontally polarized radiances,and the difference be-tween the two diagonally polarized radiances.A model has been devel-oped that describes the polarization due to reflection of and emission from a smooth surface.This model predicts the linear polarization for a landmine‘‘illuminated’’by a source that is either hotter or cooler than the surface of the landmine.The measurement setup has been used to vali-date the model.The measurements agree well with the model predic-tions and can be used for estimating the real part of the refractive index of the rubber surface of the landmine.Besides the indoor measurement, outdoor measurements have also been performed.The results of these measurements show that under the given conditions the majority of land-mines can be observed in the polarized radiance,whereas they are not clearly visible in the normal(unpolarized)radiance or the visual image.©2002Society of Photo-Optical Instrumentation Engineers.[DOI:10.1117/1.1467362] Subject terms:infrared polarization model;measurement setup;measurements; landmine detection.Paper PL-012received Sep.14,2001;revised manuscript received Dec.5,2001; accepted for publication Dec.8,2001.1IntroductionOne of the sensors used to detect landmines is a thermal infrared͑TIR͒camera.Current cameras are able to detect small temperature differences͑as low as15mK͒.Land-mines have different heat conductivity and heat capacity compared to natural backgrounds.Due to these differences in thermal properties,temperature differences between a landmine and the background may develop when the soil is heated or cooled down.The TIR radiation from other sources͑like the sun and the sky͒is reflected by the land-mine and the background.Both emission from the surfaces and reflection of the sources onto these surfaces play a role in the formation of the TIR image.TIR images of landmines in natural scenes contain clut-ter,since other͑natural͒objects like trunks,holes,and rocks also may have different thermal properties compared to the background.In the visual spectrum it is well known that unpolarized light reflected from a smooth surface be-comes polarized.1This is also true for TIR radiation.How-ever,for TIR radiation not only the reflection,but also the emission is polarized.Since in general the surfaces of land-mines are smoother than the surfaces found in a natural background,the presence of significantly polarized TIR ra-diation is an extra indication for landmines͑or other non-natural objects͒.This has been shown in previous work.2,3 In Fig.1,the relation between the various aspects of TIR polarization measurements is shown.The camera observes the scene through a polarizationfilter.The measured radi-ance,consisting of a nonlinear polarized and a polarized part,depends on the temperature of the target͑mine or background͒and the reflection of sources on the target.Currently,not a single sensor is sufficient for the detec-tion of all landmines with an acceptable false-alarm rate.4 Therefore research focuses on multisensor systems,gener-ally consisting of a ground penetrating radar͑GPR͒,a metal detector͑MD͒,and a TIR camera,for improved detection performance.5,6Using the polarization setup,the perfor-mance of the TIR camera can be improved and thus have a1021Opt.Eng.41(5)1021–1032(May2002)0091-3286/2002/$15.00©2002Society of Photo-Optical Instrumentation Engineerslarger contribution to the multisensor system.First,an introduction is given on the background of measuring TIR polarization.Furthermore,a model of TIR polarization will be presented along with the model as-sumptions.For measurements of TIR polarization,the con-structed measurement setup is described.In the next sec-tion,the indoor measurements are described.These measurements are analyzed and compared to model predic-tions.Some outdoor measurements are presented next.Fi-nally,conclusions are presented.2Polarization ModelTo describe the polarization effects,a basic model will be introduced.This model only describes the MWIR ͑3to 5m ͒polarization effects on the landmine target illuminated by a single source ͑see Fig.2͒.The polarization of the natural background is not taken into consideration.This model is based on a number of assumptions to simplify the calculations.These assumptions are:1.The material of the landmine can be described by a single refractive index for the wavelength band used ͑MWIR ͒.2.The material of the landmine is opaque,meaning that there is no transmission of radiation through the land-mine.3.The surface of the landmine is specular for reflection and obeys the Kirchoff law for radiation.4.The temperature of the landmine is constant,since the landmine is assumed to be in thermal equilibrium with its surroundings.5.The single source,which is radiating on the land-mine,is unpolarized.6.The spectral sensitivity of the MWIR camera is con-stant throughout the wavelength band,ranging from 3to 5m.7.The polarization filter is ideal for the wavelength band.8.The transmission through air is 100%and thus there is no path radiance.The source in Fig.2is assumed to be a blackbody ͑im-plying that the emission coefficient is unity for the full wavelength band ͒at a constant temperature T bb .The radi-ated electromagnetic wave can be decomposed into two perpendicular components called polarizations.One com-ponent vector E ip is in the plane of incidence;this is called parallel polarization,hence the subscript p .The plane of incidence is the plane that is spanned by the propagation direction of the incident radiation and the surface normal.The other component E is is perpendicular to the plane of incidence and is given the subscript s .These two incidence polarizations are equal in magnitude,as the source is as-sumed to be unpolarized.The thermal emission of a blackbody in the MWIR wavelength band as function of the temperature T is given by Planck’s equation 7:I BB ͑T ͒ϭ͵3m5m 2hc251e͑hc /kT ͒Ϫ1d ͓W/m 2/sr ͔,͑1͒where is the wavelength,h is Planck’s constant,k is Boltzmann’s constant,and c is the speed of light.The instantaneous electrical field (E rp ,E rs )of the TIR radiation cannot be measured by the TIR camera.The fre-quencies involved are too high to make this measurement possible.Instead,the TIR camera measures the amount of power ͑per unit area ͒incident onto the detector,which is called radiance.For electromagnetic waves with field strength E ,the radiance I is given by:I ϭ⑀a c T͵TE 2dt ͓W/m 2/sr ͔,͑2͒where ⑀a is the dielectric constant of the medium ͑air ͒,and the integration period T is large enough (T ϾϾ/c ).The radiance incident on the landmine can be described by two radiance terms,I ip and I is ,that are related to E ip and E is ,respectively.These two incident radiance terms are equalinFig.1Overview of the various aspects of infrared polarization.The camera observes the scene through a polarization filter.The mea-sured radiance and the polarization of this radiance depends on the temperature of the target (landmine or background)and the reflec-tion of sources on thetarget.Fig.2Schematic overview of the different components in the sim-plified polarization model.1022Optical Engineering,Vol.41No.5,May 2002magnitude͑as the source is not polarized͒,and the sum of these terms is equal to the blackbody radiance as given in Eq.͑1͒:I ip͑T bb͒ϭI is͑T bb͒ϭ12I BB͑T bb͒.͑3͒The surface of the landmine in Fig.2is assumed to be specular for reflection.For specular reflection,the angle of incidencei is equal to the angle of reflectionr.To de-termine the reflection coefficients,it is necessary to define the angle of transmissiont,even though the transmission is assumed to be zero.The relationship between the angle of incidencei and the angle of transmissiont is given by Snell’s law1,8:n0sin͑i͒ϭn1sin͑t͒,͑4͒where n0is the refractive index of the air and n1ϭnϩik is the refractive index of the landmine͑in complex notation͒. The refractive index of air is assumed to be1,so n0ϭ1. The refractive index of the landmine is unknown,but will be calculated in Sec.5.Part of the radiance from the source is reflected by the landmine’s surface.The amount that is reflected is given by the reflection coefficients,which differ for the two polar-ization orientations.The reflection coefficients,in terms of radiance,are given by1:p͑i͒ϭI rpI ipϭͯE rp E ipͯ2ϭͯtan͑iϪt͒tan͑iϩt͒ͯ2,͑5͒s͑i͒ϭI rsI isϭͯE rs E isͯ2ϭͯϪsin͑iϪt͒sin͑iϩtͯ2.͑6͒The remainder of the radiance from the source is either transmitted through or absorbed by the landmine.The transmission through the landmine is assumed to be zero. The theoretical situation in Fig.3is considered for obtain-ing a relationship between the absorption,reflection,and emission coefficients.In this situation a landmine with a specular surface is in thermal equilibrium with a blackbody. The incidence radiance I i(I)for angleiϭr is given by: I i͑i͒ϭI BB͑T bb͒.͑7͒This radiance is partly absorbed and partly reflected by the landmine͑transmission through the landmine is assumed to be zero͒:I i͑i͒ϭ␣͑i͒I i͑i͒ϩ͑i͒I i͑i͒.͑8͒The existence radiance I o(i)for the same part of the land-mine consists of the emitted radiation and the reflected ra-diation:I o͑i͒ϭ⑀͑i͒I BB͑T m͒ϩ͑i͒I i͑i͒.͑9͒Due to thermal equilibrium,the two temperatures T bb and T m must be the same,as do the incoming and outgoing radiances I i(i)and I o(r).This reduces Eqs.͑8͒and͑9͒to:␣͑i͒ϭ⑀͑i͒ϭ1Ϫ͑i͒.͑10͒This relationship is also known as the Kirchoff law of radiation.9,10Without giving proof,these relationships are true for both polarization directions independently.So the emission coefficients for both polarization orientations are given by:⑀p͑i͒ϭ1Ϫp͑i͒,͑11͒⑀s͑i͒ϭ1Ϫs͑i͒.͑12͒The radiance originating from the landmine consists of the reflected radiance from the source and the emitted radi-ance due to the temperature T m of the surface of the land-mine:I p͑T m,T bb,i͒ϭ12I BB͑T m͒⑀p͑i͒ϩ12I BB͑T bb͒p͑i͒ϭ12I BB͑T m͒ϩ12p͑i͓͒I BB͑T bb͒ϪI BB͑T m͔͒,͑13͒I s͑T m,T bb,i͒ϭ12I BB͑T m͒⑀s͑i͒ϩ12I BB͑T bb͒s͑i͒ϭ12I BB͑T m͒ϩ12s͑i͓͒I BB͑T bb͒ϪI BB͑T m͔͒.͑14͒Both polarized radiances depend on the difference in radiance between the blackbody and the landmine.This dif-ference can be seen as an effective illumination radiance I e(T bb,T m)incident on the landmine:I e͑T bb,T m͒ϭI BB͑T bb͒ϪI BB͑T m͒.͑15͒This radiance from Eqs.͑13͒and͑14͒passes through an ͑assumed to be͒ideal polarizer and falls subsequently onto the detector array of the camera.The detector of the camera returns a value linearly related to the incoming radiance. This value depends on the orientation of thefilter.For a given anglebetween the principal axis of the polarization filter and the horizontal axis,the radiance I c after calibra-tion,as measured by one detector element of the camera,is givenby:Fig.3The landmine in thermal equilibrium with a blackbody source.1023Optical Engineering,Vol.41No.5,May2002I c ͑͒ϭ12͓I ϩQ cos ͑2͒ϩU sin ͑2͔͒,͑16͒where ϭ0deg represents the situation that horizontally polarized radiance passes through the linear polarizer unat-tenuated.The parameters I ,Q ,and U are three of the four Stokes parameters.1The fourth parameter V defines the cir-cular polarization,is not considered in the model,and can-not be measured by only a linear polarization filter.If we define the horizontal and vertical polarization to be s-and p-polarization,respectively,then the Stokes param-eters relate to the total radiance as given in Eqs.͑13͒and ͑14͒as follows:I c ͑90deg ͒ϭ12I Ϫ12Q ϭI p ͑T m ,T bb ,i ͒,͑17͒I c ͑0deg ͒ϭ12I ϩ12Q ϭI s ͑T m ,T bb ,i ͒.͑18͒This only defines two Stokes parameters.The third param-eter U ͑diagonal polarization ͒must be zero for this situa-tion with a horizontally placed landmine and an unpolar-ized source.From Eqs.͑17͒and ͑18͒,the radiance I and the Stokes parameter Q can be calculated:I ϭI c ͑0deg ͒ϩI c ͑90deg ͒ϭ12͓s ͑i ͒ϩp ͑i ͔͒I e ͑T bb ,T m ͒ϩI BB ͑T m ͒,͑19͒Q ϭI c ͑0deg ͒ϪI c ͑90deg ͒ϭ12͓s ͑i ͒Ϫp ͑i ͔͒ϫI e ͑T bb ,T m ͒.͑20͒Often different representations are used for the polarization:LP ϭ͑Q 2ϩU 2͒1/2,͑21͒DoLP ϭLPI,͑22͒ϭ12arctan ͑U /Q ͒,͑23͒with LP being the amount of linear polarization,DoLP thedegree of polarization,and the angle of polarization ͑ori-entation of the polarization ellipse ͒.In the specific situation of Eqs.͑19͒and ͑20͒,the angle is either 0or 90deg.As is shown later on,due to thermal emission,DoLP never reaches 1for TIR.In Fig.4the Stokes parameters I and Q are plotted for a range of incidence angles i and differences between the radiance of the blackbody and the landmine.For the refrac-tive index of the landmine surface,the value 1.445ϩ0.05i is chosen.This figure clearly shows a linear rela-tion between the radiance difference and the two Stokes parameters.This model is only valid for specular reflection.In real-ity,the surface of the landmine may not be absolutely smooth.To accurately model this,it is necessary to include surface roughness using either bidirectional reflectance functions ͑BRDFs ͒or a slope approximation.11However,the model described here provides a first order approxima-tion.3Measurement SetupGenerally there are two different approaches used for themeasurement of ͑infrared ͒polarization.Either time or spa-tial division is necessary to measure up to four elements of the Stokes vector using only one focal plane.However,there are approaches that claim to measure infrared polar-ization without reducing either spatial or temporal resolu-tion;these approaches are discussed at the end of this sec-tion.With time division,different polarization images are measured sequentially.This is usually performed by mount-ing a polarization filter in front of the camera and taking a sequence of images with different polarization directions.For measurements of the full ͑four elements ͒Stokes vector,a retarder ͑for instance a quarterwave plate ͒is rotated fol-lowed by a fixed linear polarization filter.12,13This common approach of either rotating a polarizer or a retarder is re-ported by the majority of the literature.14Fig.4Model calculations for different angles of incidence i for the Stokes parameters (a)I and (b)Q as function of the radiance difference I e (T bb ,T m )between the blackbody and a landmine with a refractive index of 1.445ϩ0.05i .1024Optical Engineering,Vol.41No.5,May 2002Alternatively,the different polarizations are measured simultaneously at the cost of reduced spatial resolution.For example,every four adjacent pixels of a focal plane array ͑FPA ͒are grouped.In front of each of these four pixels a different polarization filter is mounted,each with a different orientation.This approach is followed by two parties.First,there is Nichols Research in cooperation with DERA ͑UK ͒15and the University of Alabama.16Second,there is Physics Innovations 17in cooperation with Lockheed Martin.18Because of the large development costs,this ap-proach is less common.However,due to the fact that a full set of four Stokes images can be acquired in a single frame,this approach has advantages for applications in rapidly changing environments.There are at least two other approaches that claim not to suffer from either time or spatial division of the FPA.The first one is a hyperspectral imager as made by Aerodyne.19The other is developed by FOI ͑Swedish patent FOA-R-99-01090-408-SE ͒and is a construction of a stack of detector elements,where each element only detects one polarization direction and passes the others.3.1Measurement Setup ConstructionOur approach for the measurement setup is the use of time division and a rotating polarization filter.An overview of this setup is given in Fig.5͑a ͒.The setup consists of a polarization filter ͑a wire-grid polarizer ͒,a motor to rotate the polarizer,and controller electronics ͑see Fig.6͒.In this setup different infrared cameras can be mounted.The po-larization filter has a large spectral range and can be used for either LWIR or MWIR cameras.The controller electronics have the task to synchronize the rotation of the polarization filter with the frame sync of the camera.In Fig.5͑b ͒,the motor controller is shown in more detail.For each rotation of the filter,between 6and 60frame syncs of the camera occur,depending on the set-ting of the multiplier.For the motor speed control there aretwo options:either the angle readout is divided by a num-ber or the frame sync is multiplied by a number.If the angle readout is divided,then the frequency of the phase lock loop ͑PLL ͒matches the frame sync frequency.As the frame sync frequency is usually lower than the angle read-out frequency,the controller dynamics have a tendency to become unstable.For the present setup,the choice has been made for multiplying the frame sync rather than dividing the angle readout.Because of this multiplication,the PLL works at a higher frequency and can make faster speed corrections.With this multiplication,the PLL is in lock even for the lowest rotation speed of one rotation per sec-ond ͑for a camera with a frame rate of 60Hz ͒.The angle readout is also counted ͑and reset for each rotation ͒so that the angle can be recorded for each frame sync.Once the PLL is locked in,the upper bound on the error in the angle is 3deg.One problem with this setup is that the polarization filter reflects radiation from the camera housing,the lens,and through the lens the cooled detector array.This reflected image seems to be just out of focus.So,instead of just acquiring the image of the scene,the reflected image of the camera is added to it.This effect is called narcism.There are two ways of correcting this effect.The first one is to tilt the polarizer so that it does not look at the camera,but at a uniform source.20For a small field of view ͑FOV ͒,this tilting seems possible.However,when observing mines at relatively close distance ͑a few meters ͒,a wide FOV lens is necessary and the filter has to be tilted much more ͑even up to 11deg 20͒.It is expected that this tilting may give rise to problems ͑the transmission through the filter may change ͒and seems therefore not feasible.The second approach for correcting narcism is to mea-sure and subtract the reflected image by the filter.This ap-proach is an integral part of our measurement calibration procedure and is described in Sec.4.1.4Measurements4.1System CalibrationThe camera measures the radiance over some integration period ͑fixed for these measurements at 1ms ͒.This value is digitized with a resolution of 12bits.The measured value depends on the instantaneous field of view,the aperture ͑lens diameter ͒,the efficiency of each detector element,as well as the conversion offset in the analogue to digital con-version.The fabrication process for construction offocalFig.5(a)Infrared polarization setup,consisting of a rotating polar-ization filter (a wire-grid polarizer),a motor,an infrared camera,and custom-made controller electronics.(b)The motor controller multi-plies the frame sync by a number specified by the processing com-puter.This is input to the PLL,which drives the motor such that the angle pickup has the same frequency as the multiplied frame sync.The angle pickup produces a pulse for every 3deg ofrotation.Fig.6Photo of the infrared polarization setup.1025Optical Engineering,Vol.41No.5,May 2002plane arrays͑FPAs͒sensitive to TIR radiation is not as well defined as for silicon-based FPAs.The variations in this process lead to differences in sensitivity of detector ele-ments,also called nonuniformity.When all these camera parameters are known,the mea-sured bit value can be converted to radiance in W/m2/sr. However,as already mentioned in Sec.3.1,due to the setup of camera andfilter,narcism occurs.This means that the measured radiance is the sum of͑a fraction of͒the radiance of the scene and͑a fraction of͒the reflected radiance of the camera itself.Thus,it is impossible to directly calculate the scene radiance based on only the camera parameters.We have developed a two-point calibration procedure to correct for narcism and at the same time perform a calibra-tion of the radiance.This calibration is performed sepa-rately for each detector element and thus also functions as a nonuniformity correction of the detector elements.Thefirst step is to place a blackbody with a low temperature T l in front of the polarizationfilter and acquire a sequence of images͑one for every orientation).The next step is to repeat thefirst step with a blackbody with a high tempera-ture T h.The calibrated radiance for orientationof the polarizationfilter is given for each pixel(x,y)by:I c͑,x,y͒ϭI BB͑T l͒ϩv͑,x,y͒Ϫv l͑,x,y͒v h,x,yϪl,x,yϫ͓I BB͑T h͒ϪI BB͑T l͔͒,͑24͒where v(,x,y)is the measured value of the scene͑in bits͒,and v l(,x,y)and v h(,x,y)are the measured cali-bration value of the blackbody with low and high tempera-ture,respectively.The calibration values are averaged over 30measurements to reduce the error due to camera noise. For a good calibration there must be sufficient difference between the two temperatures and they must be in the range of the apparent scene temperatures.This two-point calibration procedure removes the nar-cism from the measured images as well as corrects for transmission through thefilter.The quality of the polarizer ͑the amount of passed radiance in the cross direction͒hasnot been determined and thus will contribute to the unpo-larized radiance I.4.2Indoor Landmine MeasurementsA measurement setup,analogous to the model as shown in Fig.2,has been used to determine the polarization effects of the hot/cold blackbody source reflected by the surface of a landmine.For this experiment a dummy antipersonnel landmine,known as PMN,21is chosen͑see Fig.7͒.This PMN landmine has aflat rubber top.A piece of 10ϫ40cm of the same rubber is used for the measure-ments.Measurements are taken for six different incident angles:48.8;58.5;70.2;75.4;80.4;and85.1deg with an accuracy of around0.5deg.The distance between the rub-ber and the camera is150cm.The two-point calibration procedure͑see Sec.4.1͒is performed for each angle of incidence.The hot/cold source is varied betweenϪ10andϩ20K around ambient temperature.Each temperature of the hot/ cold source as well as the temperature of the rubber is measured with a hand ing Eq.͑1͒the radi-ances I BB(T bb)and I BB(T m)are calculated.The tempera-ture of the rubber varies only with the room temperature.A sequence of images is taken for the following temperatures around the ambient temperature:Ϫ10.0;Ϫ5.0;Ϫ2.5;0.0;5.0;10.0;15.0,and20.0K with an accuracy of0.1K.Each sequence consists of60images,with the polarizer rotated over6deg between two images.Since the camera has a frame rate of60Hz,the acquisition time is1s for each sequence.5AnalysisThe radiance as measured by the camera behind the polar-izer is given in Eq.͑16͒.This radiance is measured for a full rotation of thefilter.An estimate of the Stokes-Mueller polarization parameters I˜,Q˜,and U˜is given by:I˜ϭ2N͚jϭ1NI c͑j͒,͑25͒Q˜ϭ4N͚jϭ1NI c͑j͒cos͑2j͒,͑26͒U˜ϭ4N͚jϭ1NI c͑j͒sin͑2j͒,͑27͒where Nϭ60is the number of frames,j is the frame num-ber,andjϭ2j/N is the angle of the linear polarizer for frame j.The exact orientation of the polarization is not calibrated.The orientation is found by minimizing U˜over all the measurements.The resulting offset in angles has been used to correct the results.In Fig.8,the͑estimated͒Stokes parameter images I˜,Q˜, and U˜are shown for the angle of incidenceiϭ85deg͑a grazing angle͒with the source set at10K below the ambi-ent temperature͑297K͒.The measurement setup is similar to Fig.2.The black square in the top of the image in Fig. 8͑a͒is the blackbody.The smaller black square in the middle is the rubber of the landmine resting on top of an aluminum table.In the radiance image I˜the rubber has a lower value than the surroundings,but is higher thanthe Fig.7The dummy antipersonnel landmine PMN.Only rubber on the top of this dummy landmine was used for the indoor experi-ments.1026Optical Engineering,Vol.41No.5,May2002blackbody source.In the Q˜image the rubber is clearly visible with the lowest values.The blackbody source,which is colder than the rubber of the landmine,does nothave a lower polarized radiance Q˜,since the radiance from the blackbody source is not polarized,but it can be seendue to the edges.The U˜image is almost zero ͑observe the scale ͒and contains only camera noise.This is expected asthere are no diagonally oriented surfaces present in the scene.These measurements are performed for six different angles i and eight different temperatures T bb .This gives a total of 48sequences.For every sequence,the calculated Stokes parameters are averaged over the rubber of the land-mine.The results of these calculations are shown in Fig.9Fig.8One set of images for the blackbody set at 10K lower than the rubber of the landmine and i ϭ85deg (a grazing angle).The radiance I ˜is given in (a).The big black square in the upper half of the image is the blackbody and the small dark gray rectangle in the middle is the rubber of thelandmine.The linearly polarized radiance Q˜and U ˜are given in (b)and (c),respectively.Note that the radiance scale of (c)is a factor 5lower than the scale of (b)and that these scales are also different compared to(a).Fig.9The three Stokes parameters I ˜,Q˜,and U ˜and the angle ˜of the rubber of the landmine as a function of the radiance difference I e (T bb ,T m )and the reflection angle i .1027Optical Engineering,Vol.41No.5,May 2002as functions of the effective illumination I e (T bb ,T m )͓see Eq.͑15͔͒.Both the radiance I ˜and the polarized radiance Q ˜,re-spectively in Figs.9͑a ͒and 9͑b ͒,show a linear relation with the effective illumination for each incidence angle.The lin-ear relationship was predicted by Eqs.͑19͒and ͑20͒.The radiance I ˜increases continuously with the angle of inci-dence.The polarized radiance Q ˜,however,reaches a maxi-mum for 80deg.It is obvious from Fig.9͑c ͒that the polarized radiance U˜is zero for all effective illumination values and all angles.This can be expected from theory,since there are no diago-nally oriented surfaces and thus the polarization can only be horizontal or vertical.This also is reflected in Fig.9͑d ͒.Except for zero effective illumination,for which the angle is not well defined,the angle of polarization is either 90͑vertical ͒or 0deg ͑horizontal ͒.Using these experimental values for I ˜and Q˜,it is pos-sible to estimate the average refractive index of the rubber over the wavelength band.In fact,these measurements al-low us to make two independent estimates of the refractive index ͑compared to only one estimate in our previous publication 22͒.For each of the curves in Figs.9͑a ͒and 9͑b ͒,a best fit for the slopes is made using the linear relationships of Eqs.͑19͒and ͑20͒.This gives an estimate of both the sum ͑for the I parameter ͒and the difference ͑for the Q parameter ͒of the two reflection coefficients for each angle of incidence ͑and reflection ͒.In Fig.10the sum ͓Fig.10͑a ͔͒and the difference ͓Fig.10͑b ͔͒of the two reflection coefficients are shown.The sum of the reflection coefficients increases con-tinuously with the angle of incidence.However,the differ-ence between the reflection coefficients reaches a maxi-mum at 79deg.Note that the position of this maximum does not occur at the Brewster angle at 55deg ͑for n 1ϭ1.445),where p equals zero.1The measured radiance is fully polarized under the Brewster angle,when there is no thermal emission ͓I BB (T m )ϭ0͔and so Eqs.͑13͒and ͑14͒reduce to:I p ͑T m ,T bb ,i ͒ϭ0,͑28͒I s ͑T m ,T bb ,i ͒ϭ12s ͑i ͒I BB ͑T bb ͒.͑29͒However,due to the thermal emission of the landmine,the measured radiance will not be fully polarized:I p ͑T m ,T bb ,i ͒ϭ12I BB ͑T m ͒,͑30͒I s ͑T m ,T bb ,i ͒ϭ1I BB ͑T m ͒ϩ1s ͑i ͒I e ͑T bb ,T m ͒,͑31͒with I e (T bb ,T m )as defined in Eq.͑15͒.In Fig.11the re-lationship between the refractive index,the Brewster angle,and the angle of maximum polarization magnitude is shown.The angle of maximum polarization is almost con-stant around 78deg over a wide range of refractive indices.5.1Estimation of the Refractive IndexEquations ͑19͒and ͑20͒give the radiance I and Q as func-tion of the angle i and the effective illumination I e (T bb ,T m ),i.e.,the difference between the blackbody and rubber radiances.These equations depend on the reflection coefficients p and s and via Eq.͑4͒on therefractiveFig.10The sum (a)and the difference (b)of the reflection coefficients s and p for the rubber of the landmine.The refractive index of the model is calculated using the measurements for both the radi-ances I and Q.Fig.11The Brewster angle and the angle for which the magnitude of the polarization has a maximum as a function of the refractive index.1028Optical Engineering,Vol.41No.5,May 2002。