matlab图像处理外文翻译外文文献英文文献基于视觉的矿井救援.docx
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matlab图像处理外文翻译外文文献英文文献基于视觉的矿井救援.docx
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matlab图像处理外文翻译外文文献英文文献基于视觉的矿井救援
附录A英文原文
Scenerecognitionforminerescuerobot
localizationbasedonvision
CUIYi—an(崔益安),CAIZi-xing(蔡自兴),WANGLu(王璐)
Abstract:
AnewscenerecognitionsystemwaspresentedbasedonfuzzylogicandhiddenMarkovmodel(HMM)thatcanbeappliedinminerescuerobotlocalizationduringemergencies。
Thesystemusesmonocularcameratoacquireomni-directionalimagesofthemineenvironmentwheretherobotlocates.Byadoptingcenter-surrounddifferencemethod,thesalientlocalimageregionsareextractedfromtheimagesasnaturallandmarks。
TheselandmarksareorganizedbyusingHMMtorepresentthescenewheretherobotis,andfuzzylogicstrategyisusedtomatchthesceneandlandmark.Bythisway,thelocalizationproblem,whichisthescenerecognitionprobleminthesystem,canbeconvertedintotheevaluationproblemofHMM.Thecontributionsoftheseskillsmakethesystemhavetheabilitytodealwithchangesinscale,2Drotationandviewpoint.Theresultsofexperimentsalsoprovethatthesystemhashigherratioofrecognitionandlocalizationinbothstaticanddynamicmineenvironments.
Keywords:
robotlocation;scenerecognition;salientimage;matchingstrategy;fuzzylogic;hiddenMarkovmodel
1Introduction
Searchandrescueindisasterareainthedomainofrobotisaburgeoningandchallengingsubject[1].Minerescuerobotwasdevelopedtoenterminesduringemergenciestolocatepossibleescaperoutesforthosetrappedinsideanddeterminewhetheritissafeforhumantoenterornot.Localizationisafundamentalprobleminthisfield.Localizationmethodsbasedoncameracanbemainlyclassifiedintogeometric,topologicalorhybridones[2]。
Withitsfeasibilityandeffectiveness,scenerecognitionbecomesoneoftheimportanttechnologiesoftopologicallocalization。
Currentlymostscenerecognitionmethodsarebasedonglobalimagefeaturesandhavetwodistinctstages:
trainingofflineandmatchingonline.
Duringthetrainingstage,robotcollectstheimagesoftheenvironmentwhereitworksandprocessestheimagestoextractglobalfeaturesthatrepresentthescene.Someapproacheswereusedtoanalyzethedata-setofimagedirectlyandsomeprimaryfeatureswerefound,suchasthePCAmethod[3]。
However,thePCAmethodisnoteffectiveindistinguishingtheclassesoffeatures。
Anothertypeofapproachusesappearancefeaturesincludingcolor,textureandedgedensitytorepresenttheimage.Forexample,ZHOUetal[4]usedmultidimensionalhistogramstodescribeglobalappearancefeatures.Thismethodissimplebutsensitivetoscaleandilluminationchanges.Infact,allkindsofglobalimagefeaturesaresufferedfromthechangeofenvironment。
LOWE[5]presentedaSIFTmethodthatusessimilarityinvariantdescriptorsformedbycharacteristicscaleandorientationatinterestpointstoobtainthefeatures。
Thefeaturesareinvarianttoimagescaling,translation,rotationandpartiallyinvarianttoilluminationchanges.ButSIFTmaygenerate1000ormoreinterestpoints,whichmayslowdowntheprocessordramatically。
Duringthematchingstage,nearestneighborstrategy(NN)iswidelyadoptedforitsfacilityandintelligibility[6]。
Butitcannotcapturethecontributionofindividualfeatureforscenerecognition.Inexperiments,theNNisnotgoodenoughtoexpressthesimilaritybetweentwopatterns。
Furthermore,theselectedfeaturescannotrepresentthescenethoroughlyaccordingtothestate-of—artpatternrecognition,whichmakesrecognitionnotreliable[7].
Sointhisworkanewrecognitionsystemispresented,whichismorereliableandeffectiveifitisusedinacomplexmineenvironment.Inthissystem,weimprovetheinvariancebyextractingsalientlocalimageregionsaslandmarkstoreplacethewholeimagetodealwithlargechangesinscale,2Drotationandviewpoint。
Andthenumberofinterestpointsisreducedeffectively,whichmakestheprocessingeasier。
FuzzyrecognitionstrategyisdesignedtorecognizethelandmarksinplaceofNN,whichcanstrengthenthecontributionofindividualfeatureforscenerecognition。
Becauseofitspartialinformationresumingability,hiddenMarkovmodelisadoptedtoorganizethoselandmarks,whichcancapturethestructureorrelationshipamongthem.SoscenerecognitioncanbetransformedtotheevaluationproblemofHMM,whichmakesrecognitionrobust。
2Salientlocalimageregionsdetection
Researchesonbiologicalvisionsystemindicatethatorganism(likedrosophila)oftenpaysattentiontocertainspecialregionsinthescenefortheirbehavioralrelevanceorlocalimagecueswhileobservingsurroundings[8].Theseregionscanbetakenasnaturallandmarkstoeffectivelyrepresentanddistinguishdifferentenvironments.Inspiredbythose,weusecenter—surrounddifferencemethodtodetectsalientregionsinmulti—scaleimagespaces.Theopponenciesofcolorandtexturearecomputedtocreatethesaliencymap。
Follow-up,sub—imagecenteredatthesalientpositioninSistakenasthelandmarkregion。
Thesizeofthelandmarkregioncanbedecidedadaptivelyaccordingtothechangesofgradientorientationofthelocalimage[11]。
Mobilerobotnavigationrequiresthatnaturallandmarksshouldbedetectedstablywhenenvironmentschangetosomeextent.Tovalidatetherepeatabilityonlandmarkdetectionofourapproach,wehavedonesomeexperimentsonthecasesofscale,2Drotationandviewpointchangesetc.Fig。
1showsthatthedoorisdetectedforitssaliencywhenviewpointchanges。
Moredetailedanalysisandresultsaboutscaleandrotationcanbefoundinourpreviousworks[12]。
3Scenerecognitionandlocalization
Differentfromotherscenerecognitionsystems,oursystemdoesn'tneedtrainingoffline。
Inotherwords,ourscenesarenotclassifiedinadvance.Whenrobotwanders,scenescapturedatintervalsoffixedtimeareusedtobuildthevertexofatopologicalmap,whichrepresentstheplacewhererobotlocates。
Althoughthemap'sgeometriclayoutisignoredbythelocalizationsystem,itisusefulforvisualizationanddebugging[13]andbeneficialtopathplanning。
Solocalizationmeanssearchingthebestmatchofcurrentsceneonthemap.InthispaperhiddenMarkovmodelisusedtoorganizetheextractedlandmarksfromcurrentsceneandcreatethevertexoftopologicalmapforitspartialinformationresumingability.
Resembledbypanoramicvisionsystem,robotlooksaroundtogetomni-images。
From
Fig。
1Experimentonviewpointchanges
eachimage,salientlocalregionsaredetectedandformedtobeasequence,namedaslandmarksequencewhoseorderisthesameastheimagesequence。
ThenahiddenMarkovmodeliscreatedbasedonthelandmarksequenceinvolvingksalientlocalimageregions,whichistakenasthedescriptionoftheplacewheretherobotlocates.InoursystemEVI—D70camerahasaviewfieldof±170°.Consideringtheoverlapeffect,wesampleenvironmentevery45°toget8images.
Letthe8imagesashiddenstateSi(1≤i≤8),thecreatedHMMcanbeillustratedbyFig.2.TheparametersofHMM,aijandbjk,areachievedbylearning,usingBaulm-Welchalgorithm[14]。
Thethresholdofconvergenceissetas0.001。
Asfortheedgeoftopologicalmap,weassignitwithdistanceinformationbetweentwovertices。
Thedistancescanbecomputedaccordingtoodometryreadings。
Fig。
2HMMofenvironment
Tolocateitselfonthetopologicalmap,robotmustrunits‘eye'onenvironmentandextractalandmarksequenceL1′−Lk′,thensearchthemapforthebestmatchedvertex(scene).Differentfromtraditionalprobabilisticlocalization[15],inoursystemlocalizationproblemcanbeconvertedtotheevaluationproblemofHMM.Thevertexwiththegreatestevaluationvalue,whichmustalsobegreaterthanathreshold,istakenasthebestmatchedvertex,whichindicatesthemostpossibleplacewheretherobotis.
4Matchstrategybasedonfuzzylogic
Oneofthekeyissuesinimagematchproblemistochoosethemosteffectivefeaturesordescriptorstorepresenttheoriginalimage。
Duetorobotmovement,thoseextractedlandmarkregionswillchangeatpixellevel。
So,thedescriptorsorfeatureschosenshouldbeinvarianttosomeextentaccordingtothechangesofscale,rotationandviewpointetc.Inthispaper,weuse4featurescommonlyadoptedinthecommunitythatarebrieflydescribedasfollows。
GO:
Gradientorientation.Ithasbeenprovedthatilluminationandrotationchangesarelikelytohavelessinfluenceonit[5]。
ASMandENT:
Angularsecondmomentandentropy,whicharetwotexturedescriptors.
H:
Hue,whichisusedtodescribethefundamentalinformationoftheimage。
Anotherkeyissueinmatchproblemistochooseagoodmatchstrategyoralgorithm.Usuallynearestneighborstrategy(NN)isusedtomeasurethesimilaritybetweentwopatterns.ButwehavefoundintheexperimentsthatNNcan'tadequatelyexhibittheindividualdescriptororfeature’scontributiontosimilaritymeasurement。
AsindicatedinFig.4,theinputimageFig。
4(a)comesfromdifferentviewofFig。
4(b).ButthedistancebetweenFigs。
4(a)and(b)computedbyJeffereydivergenceislargerthanFig.4(c)。
Tosolvetheproblem,wedesignanewmatchalgorithmbasedonfuzzylogicforexhibitingthesubtlechangesofeachfeatures.Thealgorithmisdescribedasbelow.
Andthelandmarkinthedatabasewhosefusedsimilaritydegreeishigherthananyothersistakenasthebestmatch。
ThematchresultsofFigs.2(b)and(c)aredemonstratedbyFig.3。
Asindicated,thismethodcanmeasurethesimilarityeffectivelybetweentwopatterns.
Fig.3Similaritycomputedusingfuzzystrategy
5Experimentsandanalysis
Thelocalizationsystemhasbeenimplementedonamobilerobot,whichisbuiltbyourlaboratory。
ThevisionsystemiscomposedofaCCDcamera
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