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Word格式文档下载.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Word格式文档下载.docx
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canbeconvertedintotheevaluationproblemofHMM.Thecontributionsoftheseskills
makethesystemhavetheabilitytodealwithchangesinscale,2Drotationandviewpoint.
Theresultsofexperimentsalsoprovethatthesystemhashigherratioofrecognitionand
localizationinbothstaticanddynamicmineenvironments.
Keywords:
robotlocation;
scenerecognition;
salientimage;
matchingstrategy;
fuzzy
logic;
hiddenMarkovmodel
1Introduction
Searchandrescueindisasterareainthedomainofrobotisaburgeoningand
challengingsubject[1].Minerescuerobotwasdevelopedtoenterminesduring
emergenciestolocatepossibleescaperoutesforthosetrappedinsideanddetermine
whetheritissafeforhumantoenterornot.Localizationisafundamentalprobleminthis
field.Localizationmethodsbasedoncameracanbemainlyclassifiedintogeometric,
topologicalorhybridones[2].Withitsfeasibilityandeffectiveness,scenerecognition
becomesoneoftheimportanttechnologiesoftopologicallocalization.
Currentlymostscenerecognitionmethodsarebasedonglobalimagefeaturesand
havetwodistinctstages:
trainingofflineandmatchingonline.
Duringthetrainingstage,robotcollectstheimagesoftheenvironmentwhereit
worksandprocessestheimagestoextractglobalfeaturesthatrepresentthescene.Some
approacheswereusedtoanalyzethedata-setofimagedirectlyandsomeprimaryfeatures
werefound,suchasthePCAmethod[3].However,thePCAmethodisnoteffectivein
distinguishingtheclassesoffeatures.Anothertypeofapproachusesappearancefeatures
includingcolor,textureandedgedensitytorepresenttheimage.Forexample,ZHOUet
al[4]usedmultidimensionalhistogramstodescribeglobalappearancefeatures.This
methodissimplebutsensitivetoscaleandilluminationchanges.Infact,allkindsof
globalimagefeaturesaresufferedfromthechangeofenvironment.
LOWE[5]presentedaSIFTmethodthatusessimilarityinvariantdescriptorsformed
bycharacteristicscaleandorientationatinterestpointstoobtainthefeatures.The
featuresareinvarianttoimagescaling,translation,rotationandpartiallyinvariantto
illuminationchanges.ButSIFTmaygenerate1000ormoreinterestpoints,whichmay
slowdowntheprocessordramatically.
Duringthematchingstage,nearestneighborstrategy(NN)iswidelyadoptedforits
facilityandintelligibility[6].Butitcannotcapturethecontributionofindividualfeature
forscenerecognition.Inexperiments,theNNisnotgoodenoughtoexpressthe
similaritybetweentwopatterns.Furthermore,theselectedfeaturescannotrepresentthe
scenethoroughlyaccordingtothestate-of-artpatternrecognition,whichmakes
recognitionnotreliable[7].
Sointhisworkanewrecognitionsystemispresented,whichismorereliableand
effectiveifitisusedinacomplexmineenvironment.Inthissystem,weimprovethe
invariancebyextractingsalientlocalimageregionsaslandmarkstoreplacethewhole
imagetodealwithlargechangesinscale,2Drotationandviewpoint.Andthenumberof
interestpointsisreducedeffectively,whichmakestheprocessingeasier.Fuzzy
recognitionstrategyisdesignedtorecognizethelandmarksinplaceofNN,whichcan
strengthenthecontributionofindividualfeatureforscenerecognition.Becauseofits
partialinformationresumingability,hiddenMarkovmodelisadoptedtoorganizethose
landmarks,whichcancapturethestructureorrelationshipamongthem.Soscene
recognitioncanbetransformedtotheevaluationproblemofHMM,whichmakes
recognitionrobust.
2Salientlocalimageregionsdetection
Researchesonbiologicalvisionsystemindicatethatorganism(likedrosophila)often
paysattentiontocertainspecialregionsinthescenefortheirbehavioralrelevanceor
localimagecueswhileobservingsurroundings[8].Theseregionscanbetakenasnatural
landmarkstoeffectivelyrepresentanddistinguishdifferentenvironments.Inspiredby
those,weusecenter-surrounddifferencemethodtodetectsalientregionsinmulti-scale
imagespaces.Theopponenciesofcolorandtexturearecomputedtocreatethesaliency
map.
Follow-up,sub-imagecenteredatthesalientpositioninSistakenasthelandmark
region.Thesizeofthelandmarkregioncanbedecidedadaptivelyaccordingtothe
changesofgradientorientationofthelocalimage[11].
Mobilerobotnavigationrequiresthatnaturallandmarksshouldbedetectedstably
whenenvironmentschangetosomeextent.Tovalidatetherepeatabilityonlandmark
detectionofourapproach,wehavedonesomeexperimentsonthecasesofscale,2D
rotationandviewpointchangesetc.Fig.1showsthatthedoorisdetectedforitssaliency
whenviewpointchanges.Moredetailedanalysisandresultsaboutscaleandrotationcan
befoundinourpreviousworks[12].
3Scenerecognitionandlocalization
Differentfromotherscenerecognitionsystems,oursystemdoesn'
tneedtraining
offline.Inotherwords,ourscenesarenotclassifiedinadvance.Whenrobotwanders,
scenescapturedatintervalsoffixedtimeareusedtobuildthevertexofatopologicalmap,
whichrepresentstheplacewhererobotlocates.Althoughthemap'
sgeometriclayoutis
ignoredbythelocalizationsystem,itisusefulforvisualizationanddebugging[13]and
beneficialtopathplanning.Solocalizationmeanssearchingthebestmatchofcurrent
sceneonthemap.InthispaperhiddenMarkovmodelisusedtoorganizetheextracted
landmarksfromcurrentsceneandcreatethevertexoftopologicalmapforitspartial
informationresumingability.
Resembledbypanoramicvisionsystem,robotlooksaroundtogetomni-images.
From
Experimentonviewpointchanges
Fig.1
asnamedasequence,detectedandformedtobeareeachimage,salientlocalregions
hiddenThenaastheimagesequence.samelandmarksequencewhoseorderisthe
salientlocalimageMarkovmodeliscreatedbasedonthelandmarksequenceinvolvingkourIntherobotlocates.theasdescriptionoftheplacewherewhichregions,istaken
weeffect,overlap.Consideringthe170°
viewEVI-D70systemcamerahasafieldof±
toget8images.sampleenvironmentevery45°
≤8),thecreatedHMMcanbeillustratedby(1≤SiiLetthe8imagesashiddenstate
areachievedbylearning,usingBaulm-WelchbjkaijFig.2.TheparametersofHMM,and
algorithm[14].Thethresholdofconvergenceissetas0.001.
Asfortheedgeoftopologicalmap,weassignitwithdistanceinformationbetween
twovertices.Thedistancescanbecomputedaccordingtoodometryreadings.
HMMofenvironment
Fig.2
Tolocateitselfonthetopologicalmap,robotmustrunits‘eye'
onenvironmentand
extractalandmarksequenceL1′?
Lk′,thensearchthemapforthebestmatchedvertex
(scene).Differentfromtraditionalprobabilisticlocalization[15],inoursystem
localizationproblemcanbeconvertedtotheevaluationproblemofHMM.Thevertex
withthegreatestevaluationvalue,whichmustalsobegreaterthanathreshold,istaken
asthebestmatchedvertex,whichindicatesthemostpossibleplacewheretherobotis.
4Matchstrategybasedonfuzzylogic
Oneofthekeyissuesinimagematchproblemistochoosethemosteffectivefeatures
ordescriptorstorepresenttheoriginalimage.Duetorobotmovement,thoseextracted
landmarkregionswillchangeatpixellevel.So,thedescriptorsorfeatureschosenshould
beinvarianttosomeextentaccordingtothechangesofscale,rotationandviewpointetc.
Inthispaper,weuse4featurescommonlyadoptedinthecommunitythatarebriefly
describedasfollows.
GO:
Gradientorientation.Ithasbeenprovedthatilluminationandrotationchanges
arelikelytohavelessinfluenceonit[5].
ASMandENT:
Angularsecondmomentandentropy,whicharetwotexture
descriptors.
H:
Hue,whichisusedtodescribethefundamentalinformationoftheimage.
Anotherkeyissueinmatchproblemistochooseagoodmatchstrategyoralgorithm.
Usuallynearestneighborstrategy(NN)isusedtomeasurethesimilaritybetweentwo
patterns.ButwehavefoundintheexperimentsthatNNcan'
tadequatelyexhibitthe
individualdescriptororfeature'
scontributiontosimilaritymeasurement.Asindicatedin
Fig.4,theinputimageFig.4(a)comesfromdifferentviewofFig.4(b).Butthedistance
betweenFigs.4(a)and(b)computedbyJeffereydivergenceislargerthanFig.4(c).
Tosolvetheproblem,wedesignanewmatchalgorithmbasedonfuzzylogicfor
exhibitingthesubtlechangesofeachfeatures.Thealgorithmisdescribedasbelow.
Andthelandmarkinthedatabasewhosefusedsimilaritydegreeishigherthanany
othersistakenasthebestmatch.ThematchresultsofFigs.2(b)and(c)aredemonstrated
byFig.3.Asindicated,th
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