matlab中英文翻譯文獻

附錄英文原文SeenerecognitionforminerescuerobotocalizationbasedonvisionAbstract：AnewscenerecognitionsystemwaspresentedbasedonfuzzylogicandhiddenMarkovmode1(HMM)thatcanbeapp1iedinminerescuerobotlocalizationduringemergencies.Thesystemusesmonocu1arcameratoacquireomni—directionalimagesofthemineenvironmentwheretherobotlocates.Byadoptingcenter-surrounddifferencemethod,thesalientloca1imageregionsareextractedfromtheimagesasnaturallandmarks.TheselandmarksareorganizedbyusingHMMtorepresentthescenewheretherobotis,andfuzzylogicstrategyisusedtomatchthesceneand1andmark.Bythisway,theloca1izationproblem，whichisthescenerecognitionprob1eminthesystem,canbeconvertedintotheevaluationproblemofHMM.Thecontributionsoftheseski1lsmakethesystemhavetheabilitytodealwithchangesinscale,2Drotationandviewpoint.Theresultsofexperimentsa1soprovethatthesystemhashigherratioofrecognitionandloca1izationinbothstaticanddynamicmineenvironments.Keywords:robotlocation;scenerecognition；salientimage;matchingstrategy；fuzzy1ogic;hiddenMarkovmode11IntroductionSearchandrescueindisasterareainthedomainofrobotisaburgeoningandcha11engingsubject[1].Minerescuerobotwasdevelopedtoenterminesduringemergenciestolocatepossibleescaperoutesforthosetrappedinsideanddeterminewhetheritissafeforhumantoenterornot.Localizationisafundamentalprobleminthisfield.Localizationmethodsbasedoncameracanbemainlyclassifiedintogeometric,topologicalorhybridones[2].Withitsfeasibilityandeffectiveness,scenerecognitionbecomesoneoftheimportanttechnologiesoftopologicallocalization.Current1ymostscenerecognitionmethodsarebasedongloba1imagefeaturesandhavetwodistinctstages:trainingoff1ineandmatchingon1ine.Duringthetrainingstage,robotcollectstheimagesoftheenvironmentwhereitworksandprocessestheimagestoextractg1oba1featuresthatrepresentthescene.Someapproacheswereusedtoanalyzethedata-setofimagedirect1yandsomeprimaryfeatureswerefound,suchasthePCAmethod[3].However，thePCAmethodisnoteffectiveindistinguishingtheclassesoffeatures.Anothertypeofapproachusesappearancefeaturesincludingco1or,textureandedgedensitytorepresenttheimage.Forexample,ZHOUetal[4]usedmultidimensionalhistogramstodescribeg1obalappearancefeatures.Thismethodissimplebutsensitivetosca1eandi1luminationchanges.Infact,allkindsofglobalimagefeaturesaresufferedfromthechangeofenvironment.LOWE[5]presentedaSIFTmethodthatusessimilarityinvariantdescriptorsformedbycharacteristicsealeandorientationatinterestpointstoobtainthefeatures.Thefeaturesareinvarianttoimagescaling,translation,rotationandpartia1lyinvarianttoilluminationchanges.ButSIFTmaygenerate1000ormoreinterestpoints,whichmayslowdowntheprocessordramatically.Duringthematchingstage,nearestneighborstrategy(NN)iswide1yadoptedforitsfaci1ityandintel1igibility[6].Butitcannotcapturethecontributionofindividualfeatureforscenerecognition．Inexperiments,theNNisnotgoodenoughtoexpressthesimi1aritybetweentwopatterns.Furthermore,theselectedfeaturescannotrepresentthescenethoroughlyaccordingtothestate-of-artpatternrecognition,whichmakesrecognitionnotreliab1e[7].Sointhisworkanewrecognitionsystemispresented,whichismorereliableandeffectiveifitisusedinacomplexmineenvironment.Inthissystem,weimprovetheinvariancebyextractingsalientlocalimageregionsas1andmarkstoreplacethewholeimagetodealwithlargechangesinscale,2Drotationandviewpoint.Andthenumberofinterestpointsisreducedeffectively,whichmakestheprocessingeasier．FuzzyrecognitionstrategyisdesignedtorecognizethelandmarksinplaceofNN,whichcanstrengthenthecontributionofindividualfeatureforscenerecognition.Becauseofitspartialinformationresumingability,hiddenMarkovmodelisadoptedtoorganizethose1andmarks,whichcancapturethestructureorrelationshipamongthem.Soscenerecognitioncanbetransformedtotheevaluationprob1emofHMM,whichmakesrecognitionrobust.Salient1ocalimageregionsdetectionResearchesonbio1ogicalvisionsystemindicatethatorganism(likedrosophi1a)oftenpaysattentiontocertainspecialregionsinthescenefortheirbehavioralre1evanceorlocalimagecueswhileobservingsurroundings[8].Theseregionscanbetakenasnaturallandmarkstoeffectivelyrepresentanddistinguishdifferentenvironments.Inspiredbythose,weusecenter-surrounddifferencemethodtodetectsalientregionsinmulti—scaleimagespaces.Theopponenciesofcolorandtexturearecomputedtocreatethesaliencymap.Follow-up,sub-imagecenteredatthesalientpositioninSistakenasthelandmarkregion.Thesizeofthelandmarkregioncanbedecidedadaptivelyaccordingtothechangesofgradientorientationofthelocalimage[11].Mobilerobotnavigationrequiresthatnaturallandmarksshouldbedetectedstablywhenenvironmentschangetosomeextent.Tovalidatetherepeatabilityonlandmarkdetectionofourapproach，wehavedonesomeexperimentsonthecasesofscale,2Drotationandviewpointchangesetc.Fig.1showsthatthedoorisdetectedforitssaliencywhenviewpointchanges.Moredetailedanalysisandresultsaboutscaleandrotationcanbefoundinourpreviousworks[12].ScenerecognitionandlocalizationDifferentfromotherscenerecognitionsystems,oursystemdoesn'tneedtrainingoffline.Inotherwords,ourscenesarenotclassifiedinadvance.Whenrobotwanders,scenescapturedatintervalsoffixedtimeareusedtobuildthevertexofatopologicalmap,whichrepresentstheplacewhererobotlocates.Althoughthemap'sgeometriclayoutisignoredbythe1oca1izationsystem,itisusefulforvisualizationanddebugging[13]andbeneficialtopathplanning.Solocalizationmeanssearchingthebestmatchofcurrentsceneonthemap.InthispaperhiddenMarkovmodelisusedtoorganizetheextractedlandmarksfromcurrentsceneandcreatethevertexoftopologicalmapforitspartialinformationresumingabi1ity.Resembledbypanoramicvisionsystem,robotlooksaroundtogetomni-images.FromFig?1Experimentonviewpointchangeseachimage,salientlocalregionsaredetectedandformedtobeasequence,namedaslandmarksequencewhoseorderisthesameastheimagesequence.ThenahiddenMarkovmode1iscreatedbasedonthelandmarksequenceinvolvingksalientlocalimageregions，whichistakenasthedescriptionoftheplacewheretherobotlocates.InoursystemEVI-D70camerahasaviewfieldof±170°.Consideringtheoverlapeffect,wesample

environmentevery45°toget8images.Letthe8imagesashiddenstateSi(1<i<8),thecreatedHMMcanbeillustratedbyFig.2.TheparametersofHMM,aijandbjk,areachievedbylearning,usingBaulm-WelchaIgorithm[14].Thethresholdofconvergenceissetas0.001.Asfortheedgeoftopologicalmap,weassignitwithdistanceinformationbetweentwovertices.Thedistancescanbecomputedaccordingtoodometryreadings.Fig.2HMFig.2HMMofenvironmentTolocateitselfonthetopo1ogicalmap,robotmustrunits‘eye'onenvironmentandextractalandmarksequenceLI'—Lk',thensearchthemapforthebestmatchedvertex(seene).Differentfromtraditionalprobabilisticlocalization[15],inoursystemlocalizationproblemcanbeconvertedtotheeva1uationproblemofHMM.Thevertexwiththegreatesteva1uationva1ue,whichmustalsobegreaterthanathreshold,istakenasthebestmatchedvertex,whichindicatesthemostpossibleplacewheretherobotis.Matchstrategybasedonfuzzy1ogicOneofthekeyissuesinimagematchproblemistochoosethemosteffectivefeaturesordescriptorstorepresenttheoriginalimage.Duetorobotmovement,thoseextractedlandmarkregionswillchangeatpixelleve1.So,thedescriptorsorfeatureschosenshouldbeinvarianttosomeextentaccordingtothechangesofscale,rotationandviewpointetc.Inthispaper,weuse4featurescommonlyadoptedinthecommunitythatarebrieflydescribedasfollows.GO:Gradientorientation.Ithasbeenprovedthati1luminationandrotationchangesarelikelytohave1essinfluenceonit[5].ASMandENT:Angularsecondmomentandentropy,whicharetwotexturedescriptors.H:Hue,whichisusedtodescribethefundamentalinformationoftheimage.Anotherkeyissueinmatchproblemistochooseagoodmatchstrategyora1gorithm.Usuallynearestneighborstrategy(NN)isusedtomeasurethesimilaritybetweentwopatterns.ButwehavefoundintheexperimentsthatNNcan'tadequatelyexhibittheindividualdescriptororfeature'scontributiontosimilaritymeasurement.AsindicatedinFig.4,theinputimageFig.4(a)comesfromdifferentviewofFig.4(b).ButthedistancebetweenFigs.4(a)and(b)computedbyJeffereydivergenceislargerthanFig.4(c).Toso1vetheproblem,wedesignanewmatchalgorithmbasedonfuzzylogicforexhibitingthesubt1echangesofeachfeatures.Thealgorithmisdescribedasbelow.Andthelandmarkinthedatabasewhosefusedsimilaritydegreeishigherthananyothersistakenasthebestmatch.Thematchresu1tsofFigs.2(b)and(c)aredemonstratedbyFig.3.Asindicated,thismethodcanmeasurethesimilarityeffectivelybetweentwopatterns.Fig.3Similaritycomputedusingfuzzystrategy5ExperimentsandanalysisThe1ocalizationsystemhasbeenimplementedonamobilerobot,whichisbui1tbyourlaboratory.ThevisionsystemiscomposedofaCCDcameraandaframe-grabberIVC-4200.Thereso1utionofimageissettobe400x320andthesamplefrequencyissettobe10frames/s.Thecomputersystemiscomposedof1GHzprocessorand512Mmemory,whichiscarriedbytherobot.Presentlytherobotworksinindoorenvironments.BecauseHMMisadoptedtorepresentandrecognizethescene,oursystemhastheabilitytocapturethediscriminationaboutdistributionofsalientlocalimageregionsanddistinguishsimilarsceneseffectively.Table1showstherecognitionresultofstaticenvironmentsincluding51anewaysandasilo.10scenesareselectedfromeachenvironmentandHMMsarecreatedforeachscene．Then20scenesarecol1ectedwhentherobotenterseachenvironmentsubsequentlytomatchthe60HMMsabove.Inthetable,“truth”meansthatthescenetobelocalizedmateheswiththerightscene(theevaluationvalueofHMMis30%greaterthanthesecondhighevaluation).“Uncertainty”meansTOC\o"1-5"\h\zthattheevaluationvalueofHMMisgreaterthanthesecondhighevaluationunder10%.“Errormatch”meansthatthescenetobelocalizedmatcheswiththewrongscene.Inthetable,theratiooferrormatchis0.Butitispossiblethatthescenetobelocalizedcan'tmatchanyscenesandnewvertexesarecreated.Furthermore,the“ratiooftruth”aboutsi1oislowerbecausesalientcuesarefewerinthiskindofenvironment.Intheperiodofautomaticexploring,simi1arscenescanbecombined.Theprocesscanbesummarizedas:whenloca1izationsucceeds,thecurrentlandmarksequenceisaddedtotheaccompanyingobservationsequenceofthematchedvertexun-repeatedlyaccordingtotheirorientation(includingtheangleoftheimagefromwhichthesalientlocalregionandtheheadingoftherobotcome)．TheparametersofHMMarelearnedagain.Comparedwiththeapproachesusingappearancefeaturesofthewho1eimage(Method2,M2),oursystem(M1)useslocalsalientregionstolocalizeandmap,whichmakesithavemoretoleranceofscale，viewpointchangescausedbyrobot'smovementandhigherratioofrecognitionandfeweramountofverticesonthetopologicalmap.So,oursystemhasbetterperformanceindynamicenvironment.ThesecanbeseeninTable2.Laneways1,2,4,5areinoperationwheresomeminersareworking,whichpuzzletherobot.6Conclusions1)Salientloca1imagefeaturesareextractedtoreplacethewhol??????q?q?*eimagetoparticipatemrecognition,whichimprovetheto1eranceofchangesinscale,2Drotationandviewpointofenvironmentimage.2)Fuzzylogicisusedtorecognizethelocalimage,andemphasizetheindividualfeature'contributiontorecognition,whichimprovesthereliabilityoflandmarks.HMMisusedtocapturethestructureorrelationshipofthoselocalimages,whichconvertsthescenerecognitionproblemintotheevaluationproblemofHMM.Theresultsfromtheaboveexperimentsde/r