《信息科學(xué)類專業(yè)英語》課件第20章

Lesson20RecentAdvancesinComputerVision

(第二十課計(jì)算機(jī)視覺的新進(jìn)展)

Vocabulary(詞匯)ImportantSentences(重點(diǎn)句)QuestionsandAnswers(問答)Problems(問題)ReadingMaterial(閱讀材料)

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.非破壞(性)的,不破壞的,(檢驗(yàn)方法)無損的。

2.?chronicadj.(=chronical)慢性的;緩慢的;長(zhǎng)期的;積習(xí)成癖的[英俚]劇烈的,緊張的;嚴(yán)重的;(天氣等)惡劣的。

3.?bottleneckn.瓶頸；困難;障礙；隘路;狹道;難關(guān)；薄弱環(huán)節(jié)；涌塞(現(xiàn)象);影響生產(chǎn)流程的因素(如缺少原料等)。

4.?dedicatedcomputers專用計(jì)算機(jī)。

5.?off-the-shelf現(xiàn)貨供應(yīng)：在存貨商品中能得到的；非定制的。

Vocabulary

6.?daughterboard子板，子插件。指一塊主板的附屬電路板，通常包括插槽、插座、引腳、連接件等其他附屬部分，不同于標(biāo)準(zhǔn)的PCI或ISA等標(biāo)準(zhǔn)板。

7.?peripheraladj.周界的;外圍的;外部的;邊緣的；非本質(zhì)的;不深入的;膚淺的；【解】(神經(jīng))末梢區(qū)域的。

8.?surveillancen.監(jiān)視,看守；監(jiān)督,管制。

9.?countermeasuren.反措施，對(duì)抗手段，對(duì)策：對(duì)抗或抵消某手段或行為的策略或行動(dòng)。

10.?trajectoryn.(拋射體的)軌道,軌跡,彈道;流軌;【數(shù)】軌線。

[1]Sinceitsstart,computervisionhasappearedasacomputationallyintensiveandalmostintractablefieldbecauseitsalgorithmsrequireaminimumofhundredsofMIPS(millionsofinstructionspersecond)tobeexecutedinacceptablerealtime.

從一開始，計(jì)算機(jī)視覺就展示為一種計(jì)算密集而幾乎難處理的領(lǐng)域，因?yàn)樾枰辽倜棵雸?zhí)行幾百萬條數(shù)量級(jí)的指令，它的算法才能達(dá)到一個(gè)可接受的實(shí)時(shí)要求。ImportantSentences

[2]DedicatedcomputerssuchastheMPPhavealwaysreceivedacoldreceptionfromindustrybecausetheywereexpensive,cumbersome,anddifficulttoprogram.

像MPP這樣的專用計(jì)算機(jī)，由于它們價(jià)格昂貴、體積笨重且難以編程而受到工業(yè)界的冷遇。

[3]Thebasicideaisthatimportantobjectssuchasactorsshouldretainthehighestvisualquality,whileobjectsinthebackgroundcanbeencodedwithlowerqualitytosavebytes.

基本的想法是重要的目標(biāo)(如行動(dòng)者)將保留最高的視覺質(zhì)量，而背景目標(biāo)則用較低的質(zhì)量編碼以便減少存儲(chǔ)字節(jié)數(shù)量。

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

這樣的圖像自動(dòng)地檢測(cè)景物中的人員及/或車輛或其他感興趣的目標(biāo)，并將它們分為不同的種類，如人員、汽車、自行車或公共汽車，提取它們的軌跡，識(shí)別肢體和手臂的位置，并提供一些行為分析的形式。

[5]Althoughmanufacturersandoperatorsofsurveillancesystemshaveoftenbeenreluctanttoacceptinnovation,recentresultsfromresearchlaboratoriesofmajorcompaniesprovethatthesesystemsarenowreliable,economical,andreadyforcommercialization.

雖然監(jiān)督系統(tǒng)的制造商和操作員通常不愿意接受創(chuàng)新，但主要公司的研究工作實(shí)驗(yàn)室近期的結(jié)果證明這些系統(tǒng)現(xiàn)在是可靠的、經(jīng)濟(jì)的且已經(jīng)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