《信息科学类专业英语》课件第20章

Lesson20RecentAdvancesinComputerVision

(第二十课计算机视觉的新进展)

Vocabulary(词汇)ImportantSentences(重点句)QuestionsandAnswers(问答)Problems(问题)ReadingMaterial(阅读材料)

Computervisionisthebranchofartificialintelligencethatfocusesonprovidingcomputerswiththefunctionstypicalofhumanvision.Todate,computervisionhasproducedimportantapplicationsinfieldssuchasindustrialautomation,robotics,biomedicine,andsatelliteobservationofEarth.Inthefieldofindustrialautomationalone,itsapplicationsincludeguidanceforrobotstocorrectlypickupandplacemanufacturedparts,nondestructivequalityandintegrityinspection,andon-linemeasurements.Untilafewyearsago,chronicproblemsaffectedcomputer-visionsystemsandpreventedtheirwidespreadadoption.Sinceitsstart,computervisionhasappearedasacomputationallyintensiveandalmostintractablefieldbecauseitsalgorithmsrequireaminimumofhundredsofMIPS(MillionsofInstructionsPer-Second)tobeexecutedinacceptablerealtime.[1]Eventheinput/outputofhigh-resolutionimagesatvideoratewastraditionallyabottleneckforcommoncomputingplatformssuchaspersonalcomputersandworkstations.Tosolvetheseproblems,theresearchcommunityhasproducedanimpressivenumberofdedicatedcomputer-visionsystems.OnesuchfamoussystemwastheMassivelyParallelProcessor(MPP),designedattheGoddardSpaceFlightCenterin1983andoperatedthereuntil1991.TheMPPusedanarrayof16,384single-bitprocessorsandwascapableatpeakperformanceof250millionfloating-pointoperations/s—animpressivefeatatthetime.

DedicatedcomputerssuchastheMPPhavealwaysreceivedacoldreceptionfromindustrybecausetheywereexpensive,cumbersome,anddifficulttoprogram.[2]Inrecentyears,however,increasedperformanceatthesystemlevel—fastermicroprocessors,fasterandlargermemories,andfasterandwiderbuses—hasmadecomputervisionaffordableonawidescale.Fastmicroprocessorsanddigital-signalprocessorsarenowavailableasoff-the-shelfsolutions,andsomeofthemcanexecutecalculationsatratesofthousandsofMIPS.TheTexasInstrumentsC6414processor,forexample,runsat600MHzandcanachieveapeakperformanceof4,800MIPS.HighspeedserialbusessuchastheIEEE1394andUSB2.0arecapableoftransferringhundredsofmegabitspersecond,aratethatgreatlyexceedstherequirementsofanycommonhigh-resolutionvideocamera.Thesebusesarealreadyintegratedintothemostrecentpersonalcomputerchipsetsorareavailableasinexpensivedaughterboards.Moreover,videocamerashavegonealmostcompletelytodigital,andtheycomeinseveralpricerangesandtypes.ConsumercamcordersarebasedonstandardssuchastheDigitalVideo(DV),whichprovidesvideoswith720×480pixels/frameatarateof30frames/s.EvenWebcamscannowprovideimagesofsatisfactoryqualityatpricesstartingaslowas$25.

Theavailabilityofaffordablehardwareandsoftwarehasopenedthewayfornew,pervasiveapplicationsofcomputervision.Theseapplicationshaveonefactorincommon.Theytendtobehuman-centered;thatis,eitherhumansarethetargetsofthevisionsystemortheywanderaboutwearingsmallcameras,orsometimesboth.Visionsystemshavebecomethecentralsensorinapplicationssuchas

Human-ComputerInterfaces(HCIs),thelinksbetweencomputersandtheirusers.

augmentedperception,toolsthatincreasenormalperceptioncapabilitiesofhumans.

automaticmediainterpretation,whichprovidesanunderstandingofthecontentofmoderndigitalmedia,suchasvideosandmovies,withouttheneedforhumaninterventionorannotation.

videosurveillanceandbiometrics.1Human-computerinterfaces

ThebasicideabehindtheuseofcomputervisioninHCIsisthatinseveralapplications,computerscanbeinstructedmorenaturallybyhumangesturesthanbytheuseofakeyboardormouse.Inoneinterestingapplication,computerscientistJamesL.CrowleyoftheNationalPolytechnicalInstituteofGrenobleinFranceandhiscolleaguesusedhumaneyemovementstoscrollacomputerscreenupanddown.Acameralocatedontopofthescreentrackedtheeyemovements.TheFrenchresearchersreportedthatatrainedoperatorcouldcompleteagiventask32%fasterbyusinghiseyesratherthanakeyboardormousetodirectscreenscrolling.Ingeneral,usingcamerastosensehumangesturesismucheasierthanmakinguserswearcumbersomeperipheralssuchasdigitalgloves.

AnotherinterestingexampleofanHCIapplicationcanbedownloadedhereforpersonaltesting,providedaWebcamispluggedintoyourpersonalcomputer.Thisapplication—calledNouse,fornoseasamouse—tracksthemovementsofyournose,andwasdevelopedbyDmitryGorodnichy.YoucanplayNosePong,anose-drivenversionofthePongvideogame(Fig.1),ortestyourabilitytopaintwithyournoseortowritewithyournose.Althoughthisapplicationisslantedtowardfun,itisaconvincingdemonstrationofthepotentialusesofcamerasasnaturalinterfaces.Inindustry,forexample,anoperatormightquicklystopaconveyorbeltwithaspecificgesturedetectedbyacamerawithoutneedingtophysicallypushabutton,pullalever,orcarryaremotecontrol.Fig.1Acameratracksthepointofeachplayer’snoseclosesttothecameraandlinksittothe

red“bat”at

thetop(orbottom)ofthetabletoreturnthecomputerballacrossthe“net.”(InstituteforInformationTechnologyNationalResearchCouncilCanada;UniversityofTechnology,Sydney,Australia)

Camerascouldalsobecomepowerfulperipheralsfortheso-calledintelligenthome.Acameralocatedinyourlivingroomwouldperformseveraltasks,startingwithsensingahumanpresenceandthenturningthelightsonandtheheatup.Indeed,camerascouldreplacethemanyhard-to-findremotecontrolsaroundtoday’shomes,provideenvironmentalsurveillance,andturntheTVoffwhenyoufallasleepinyourfavouritearmchair.2TheVoice

AnotherapplicationisThevOICe,developedatPhilipsResearchLaboratories(Eindhoven,TheNetherlands)byPeterB.L.Meijerandavailableonlinefortesting.ThevOICeprovidesasimpleyeteffectivemeansofaugmentedperceptionforpeoplewithpartiallyimpairedvision.Inthevirtualdemonstration,thecameraaccompaniesyouinyourwanderings.Thecameraperiodicallyscansthesceneinfrontofyouandturnsimagesintosounds,usingdifferentpitchesandlengthstoencodeobjects’positionandsize.3Mediainterpretation

Theuseofcomputervisionforautomaticmediainterpretationassistsusersinsearchingforspecificscenesandshotsotherwisenotannotatedinthevideo-sceneindexes.Forexample,imagescontainingfacescanbeautomaticallydistinguishedfromotherimages,astheresultsoftheFaceDetectionProjectledbyHenrySchneidermanandTakeoKanadeatCarnegieMellonUniversity(CMU)prove.TheCMUfacedetectorisconsideredthemostaccurateforfrontalfacedetectionandisalsoreliableforfacialprofilesandthree-quarterimages.Manyexamplesareavailablehere-oneisshowninFig.1,top—andanyonecansubmitanimagewhichwillprocesstheimageovernightanddepictalldetectedfaceswithaboxaroundthem.

However,computervisioncandomuchmoreformultimedia.Forexample,itisaninvaluablesupporttorecentmultimediastandardsaimedatcompressingdigitalvideos—reducingtheirsizeinbytes—whilestillretainingacceptablevisualquality.OnesuchstandardisMPEG-4fromtheMovingPictureExpertGroup,whichallowsthecompressionofdifferentobjectsinascenewithspecificcompressionlevelsinsuchawayastoadjustthetrade-offbetweenspacereductionandvisualqualityonaper-objectbasis.Thebasicideaisthatimportantobjectssuchasactorsshouldretainthehighestvisualquality,whileobjectsinthebackgroundcanbeencodedwithlowerqualitytosavebytes.[3]Nonetheless,MPEG-4issilentonhowtoseparateavideointotheobjectsofwhichitiscomposed.Hereagain,computervisioncanhelpwithavarietyoftechniquesthatperformthetaskautomatically.4VideoSurveillance

Perhapsthemostdevelopedmodernapplicationofcomputervisionisvideosurveillance.Longgonearethedayswhenvideosurveillancemeantlow-resolution,black-and-white,analogclosed-circuittelevision.Nowadays,computervisionenablestheintegrationofviewsfrommanycamerasintoasingle,consistent“superimage”.Suchanimageautomaticallydetectssceneswithpeopleand/orvehiclesorothertargetsofinterest,classifiesthemincategoriessuchaspeople,cars,bicycles,orbuses,extractstheirtrajectories,recognizeslimbandarmpositions,andprovidessomeformofbehavioranalysis.[4]

Theanalysisreliesonalistofpreviouslyspecifiedbehaviorsoronstatisticalobservationssuchasfrequent-versus-infrequentbehaviors.Thebasicgoalisnottocompletelyreplacesecuritypersonnelbuttoassisttheminsupervisingwiderareasandfocusingtheirattentiononeventsofinterest.Althoughthecriticalissueofprivacymustbeaddressedbeforesocietywidelyadoptsthesevideosurveillancesystems,therecentneedforincreasedsecurityhasmadethemmorelikelytowingeneralacceptance.Inaddition,severaltechnicalcountermeasurescanbetakentopreventprivacyabuses,suchasprotectingaccesstovideofootagebywayofpasswordsandencryption.

AttheUniversityofTechnologyinSydney,Australia,wehavedevelopedandtestedasystemthatcandetectsuspiciouspedestrianbehaviorinparkinglots.Ourapproachisbasedontheassumptionthatasuspiciousbehaviorcorrespondstoanindividual’serraticwalkingtrajectory.Therationalebehindthisassumptionisthatapotentialoffenderwillwanderaboutandstopbetweendifferentcarstoinspecttheircontents,whereasnormaluserswillmaintainamoredirectpathoftravel.Thefirststepconsistsofdetectingallthemovingobjectsinthescenebysubtractinganestimated“backgroundimage”—onethatrepresentsonlythestaticobjectsinthescene—fromthecurrentframe(Fig.2(a)and(b)).Thenextstepistodistinguishpeoplefrommovingvehiclesonthebasisofaformfactor,suchastheheight:widthratio,andtolocatetheirheadsasthetopregionintheirsilhouette.Inthisway,thehead’sspeedateachframeisautomaticallydetermined.Then,aseriesofspeedsamplesarerepeatedlymeasuredforeachpersoninthescene.Eachseriescoversanintervalofabout10s,whichisenoughtodetectsuspiciousbehaviorpatterns(Fig.3,below).Fig.2Thisparking-lotsurveillancesystemsubtractsthestaticbackgroundimage,

distinguishesapersonfrommovingvehicles,locatesthehead,andcalculatesthespeedoftheheadineachframe.Fig.3Examplesofthespeedofthehead(inpixelsperframe)ofapersonintheparkinglotexhibitingnormalbehavior(a)andabnormalbehavior(b).Suchvideosurveillancemightalertasecurityguardtoapossiblecarthief.

Finally,aneuralnetworkclassifier,trainedtorecognizethesuspiciousbehaviors,providesthebehaviorclassification.Intheexperimentsweperformed,thesystemachievedgoodaccuracy,withareasonablylimitednumberoffalsedismissalsandfalsealarms—4%and2%,respectively,amongmorethan100testsamples.Althoughmanufacturersandoperatorsofsurveillancesystemshaveoftenbeenreluctanttoacceptinnovation,recentresultsfromresearchlaboratoriesofmajorcompaniesprovethatthesesystemsarenowreliable,economical,andreadyforcommercialization.[5]OneexampleisDETERfromHoneywellLabs,aprototypeurban-surveillancesystem.

Forthosewhowanttobuildtheirownsurveillancesystems,anenormousamountofequipmentisavailable.WebsitesofmanufacturerssuchasSony,Axis,Pelco,andmanyothersofferawiderangeofcameras.Youcanfindnetworkcamerasstartingatlessthan$500thatcanbesimplypluggedintoanynetwork,suchasaTCP-IP,whichcancarryafullWebserverandallowcameraframestobedownloadedandprocessed.Adjustablepan-tilt-zoomcamerascanbeusedtopointandfocusonspecifictargetsoverwidesurveyareas.Andifcablingposesaproblembecauseofcameralocation,wirelessversionsareavailableoff-the-shelf.Computervision,alreadyausefulaidinseveralindustrialprocesses,willfindincreasingusesascompaniesdevelopnewapplicationsinareassuchasHCI,augmentedperception,andautomaticmediainterpretation.Itspotentialtoimproveplantandpublicsafetyisattractingincreasingattentionintoday’ssecurity-consciousworld.

1. nondestructiveadj.非破坏(性)的,不破坏的,(检验方法)无损的。

2. chronicadj.(=chronical)慢性的;缓慢的;长期的;积习成癖的[英俚]剧烈的,紧张的;严重的;(天气等)恶劣的。

3. bottleneckn.瓶颈；困难;障碍；隘路;狭道;难关；薄弱环节；涌塞(现象);影响生产流程的因素(如缺少原料等)。

4. dedicatedcomputers专用计算机。

5. off-the-shelf现货供应：在存货商品中能得到的；非定制的。

Vocabulary

6. daughterboard子板，子插件。指一块主板的附属电路板，通常包括插槽、插座、引脚、连接件等其他附属部分，不同于标准的PCI或ISA等标准板。

7. peripheraladj.周界的;外围的;外部的;边缘的；非本质的;不深入的;肤浅的；【解】(神经)末梢区域的。

8. surveillancen.监视,看守；监督,管制。

9. countermeasuren.反措施，对抗手段，对策：对抗或抵消某手段或行为的策略或行动。

10. trajectoryn.(抛射体的)轨道,轨迹,弹道;流轨;【数】轨线。

[1]Sinceitsstart,computervisionhasappearedasacomputationallyintensiveandalmostintractablefieldbecauseitsalgorithmsrequireaminimumofhundredsofMIPS(millionsofinstructionspersecond)tobeexecutedinacceptablerealtime.

从一开始，计算机视觉就展示为一种计算密集而几乎难处理的领域，因为需要至少每秒执行几百万条数量级的指令，它的算法才能达到一个可接受的实时要求。ImportantSentences

[2]DedicatedcomputerssuchastheMPPhavealwaysreceivedacoldreceptionfromindustrybecausetheywereexpensive,cumbersome,anddifficulttoprogram.

像MPP这样的专用计算机，由于它们价格昂贵、体积笨重且难以编程而受到工业界的冷遇。

[3]Thebasicideaisthatimportantobjectssuchasactorsshouldretainthehighestvisualquality,whileobjectsinthebackgroundcanbeencodedwithlowerqualitytosavebytes.

基本的想法是重要的目标(如行动者)将保留最高的视觉质量，而背景目标则用较低的质量编码以便减少存储字节数量。

[4]Suchanimageautomaticallydetectssceneswithpeopleand/orvehiclesorothertargetsofinterest,classifiesthemincategoriessuchaspeople,cars,bicycles,orbuses,extractstheirtrajectories,recognizeslimbandarmpositions,andprovidessomeformofbehavioranalysis.

这样的图像自动地检测景物中的人员及/或车辆或其他感兴趣的目标，并将它们分为不同的种类，如人员、汽车、自行车或公共汽车，提取它们的轨迹，识别肢体和手臂的位置，并提供一些行为分析的形式。

[5]Althoughmanufacturersandoperatorsofsurveillancesystemshaveoftenbeenreluctanttoacceptinnovation,recentresultsfromresearchlaboratoriesofmajorcompaniesprovethatthesesystemsarenowreliable,economical,andreadyforcommercialization.

虽然监督系统的制造商和操作员通常不愿意接受创新，但主要公司的研究工作实验室近期的结果证明这些系统现在是可靠的、经济的且已经可以商品化。

(1) Whataffectedcomputer-visionsystemsandpreventedtheirwidespreadadoptionforalongtime?()

A. Chronicproblems.

B. Spacecomplexity.

C. Timecomplexity.

D. Dedicatedcomputers.QuestionsandAnswers

(2) DedicatedcomputerssuchastheMPPhavealwaysreceivedacoldreceptionfromindustrybecausetheywere().

A. expensive

B. cumbersome

C. difficulttoprogram

D. alloftheabove

(3) What’sthemostdevelopedmodernapplicationofcomputervision?()

A. MPEG-4standard.

B. Videosurveillance.

C. Intelligenthome.

D. FaceDetectionProjectatCMU.

(4) Nowadays,computervisionenablestheintegrationofviewsfrommanycamerasintoasingle,consistent“superimage”.What’sthesuperimagemean?()

A. Imagewithhugesizeindimensions.

B. Asetofmethodsofupscalingvideoorimages.

C. Suchanimageautomaticallydetectssceneswithtargetsofinterest.

D. Integrationofviewsfrommanycameras.

(5) AsFig.3shows,theparking-lotsurveillancesystem().

A. locatesthehead,subtractsthestaticbackgroundimage,distinguishesapersonfrommovingvehicles,andcalculatesthespeedoftheheadineachframe

B. distinguishesapersonfrommovingvehicles,subtractsthestaticbackgroundimage,locatesthehead,andcalculatesthespeedoftheheadineachframe

C. distinguishesapersonfrommovingvehicles,locatesthehead,subtractsthestaticbackgroundimage,andcalculatesthespeedoftheheadineachframe

D. subtractsthestaticbackgroundimage,distinguishesapersonfrommovingvehicles,locatesthehead,andcalculatesthespeedoftheheadineachframe

1. Whytheauthorgavesuchadisparatepicturesthatcomputervisionhasappearedasacomputationallyintensiveandalmostintractablefield？

2. Militaryapplicationsareprobablyoneofthelargestareasforcomputervision;doyouthinkittrueornot?Problems

Computervisionisthescience(somesayart)ofprogrammingacomputertoprocess,andultimatelyunderstand,imagesandvideo.Itcanbeviewedassignalprocessingappliedto2D(images),3D(videos),orhigherdimensions.Thisviewhighlightsoneofthemaindifficulties;moderncomputershavea‘serial’design,meaningtheycanonlyprocessonepieceofdataatatime.‘Parallel’processingcomputerswouldbemoresuitableformultidimentionalsignalssuchasvisiontask,andindeed,thisishowthehumanvisualsystemisorganised.ReadingMaterial

ComputerVisionisoneoftheultimateunsolvedproblemsincomputerscience,andsolvingit,orevensmallpartsofit,createsexcitingnewpossibilitiesintechnology,engineeringandevenentertainment.Todaysexamplesrunfromvisualaidsfortheblind,torobotics,tothenewSonyEyeToy!Thefutureofthisquicklydevelopingfieldisonlylimitedbyourimagination.

Mycomputerhasawebcam,doesn’tthatmeanitcansee?No!Awebcamordigitalcameraallowsacomputertocaptureimagesorvideo,recorditandreproduceitonthemonitor.Thisiswhereyoucanseeit.Thecomputernever‘sees’thevideobecauseitcannotunderstandtheinformationintheimageorvideo.Itslikeowningabookwithoutbeingabletoread.Computervisionisaboutprogrammingcomputerstobeableto‘read’theinformationinvisualdata.

ThegoalofComputervisionistoprocessimagesacquiredwithcamerasinordertoproducearepresentationofobjectsintheworld.

Therealreadyexistsanumberofworkingsystemsthatperformpartsofthistaskinspecializeddomains.Forexample,amapofaacityoramountainrangecanbeproducedsemiautomaticallyfromasetofaerialimages.Arobotcanusetheseveralimageframespersecondproducedbyoneortwovideocamerastoproduceamapofitssurroundingsforpathplanningandobstacleavoidance.Aprintedcircuitinspectionsystemmaytakeonepictureperboardonaconveyerbeltandproduceabinaryimageflaggingpossiblefaultysolderingpointsontheboard.

AzipcodereadertakessinglesnapshotsofenvelopesandtranslatesahandwrittennumberintoanASCIIstring.Asecuritysystemcanmatchoneorafewpicturesofafacewithadatabaseofknownemployeesforrecognition.

However,thegeneric“VisionProblem”isfarfrombeingsolved.Noexistingsystemcancomeclosetoemulatingthecapabilitiesofahuman.Systemssuchastheonesdescribedabovearefundamentallybrittle:Assoonastheinputdeviateseversoslightlyfromtheintendedformat,theoutputbecomesalmostinvariablymeaningless.Ifwedidnothaveaproofofexistenceofaverypowerful,generalandflexiblesysteminourownretinasandvisualcortices,theresearchofthepastquarterofacenturywouldseemtoindicatethatthetaskofbuildingrobustvisionsystemsishopeless.

Visionisthereforeoneoftheproblemsofcomputersciencemostworthyofinvestigationbecauseweknowthatitcanbesolved,yetwedonotknowhowtosolveitwell.Infact,tosolvethe“generalvisionproblem”wewillhavetocomeupwithanswerstodeepandfundamentalquestionsaboutrepresentationandcomputationatthecoreofhumanintelligence.

Oneofthemostprominentapplicationfieldsismedicalcomputervisionormedicalimageprocessing.Thisareaischaracterizedbytheextractionofinformationfromimagedataforthepurposeofmakingamedicaldiagnosisofapatient.Generally,imagedataisintheformofmicroscopyimages,X-rayimages,angiographyimages,ultrasonicimages,andtomographyimages.Anexampleofinformationwhichcanbeextractedfromsuchimagedataisdetectionoftumours,arteriosclerosisorothermalignchanges.Itcanalsobemeasurementsoforgandimensions,bloodflow,etc.Thisapplicationareaalsosupportsmedicalresearchbyprovidingnewinformation,e.g.,aboutthestructureofthebrain,oraboutthequalityofmedicaltreatments.

Asecondapplicationareaincomputervisionisinindustry,sometimescalledmachinevision,whereinformationisextractedforthepurposeofsupportingamanufacturingprocess.Oneexampleisqualitycontrolwheredetailsorfinalproductsarebeingautomaticallyinspectedinorderto