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Z.Panetal.Eds.ICAT2006,LNCS4282,pp.785–795,2006.©SpringerVerlagBerlinHeidelberg2006RecognitionandLocationofFruitObjectsBasedonMachineVisionHuiGu,YayaLu,JilinLou,andWeitongZhangInformationEngineeringCollege,ZhejiangUniversityofTechnology,310014,Hangzhou,Chinaghzjut.edu.cn,{oo327,phonixlou,seasonzwt}163.comAbstract.Thispaperdiscussedthelowlevelmachinevisiononfruitandvegetableharvestingrobot,introducedtherecognitionandlocationoffruitandvegetableobjectsundernaturescenes,putforwardanewsegmentationmethodcombinedwithseveralcolormodels.Whatsmore,itpresentedanovelconceptionforthedeterminationoftheabscissionpoint,successfullyresolvedthelocationofcenterandabscissionpointwhenthefruitwerepartiallyoccluded.Meanwhile,bythetechniqueofgeometry,itsettledthelocationsoftheabscissionpointwhenthefruitgrewaskew.Itprovedgoodeffectunderthenaturescene.KeywordsMachinevision,fruitobject,recognition,location.1IntroductionDuringtheprocessofhumanconqueringtheNature,rebuildingtheNatureandpromotingthesociety,humansarefacingtheproblemofabilitylimitation.Asaresult,humanshavebeenseekingfortherobotstosubstitutethemantocompletecomplicatedtasks,andtheintelligentrobotisthebestchoice.Asweallknow,visionisthemainwayofhumansapperceivingtheworld.About80informationisgotthroughvision.So,itisvitaltograntvisionfunctionforintelligentrobots.Here,wecandefinethemachinevisionasfollowsitisabletoproducesomedescriptionaboutthecontentoftheimageafterprocessingtheinputimage1.Therearemanyfieldsrelatedwithmachinevision.So,italsohasawideapplicationinvariousaspects,frommedicalimagetoremotesensedimage,fromindustrialinspectiontoagriculturalareas,etc.Thefruitandvegetableharvestingrobotwhichwearegoingtodiscussisonekindofautomaticmechanicalharvestingsystemspossessingtheperceptiveability,canbeprogrammedtoharvest,transferandpackthecrops2.Duringtheprocessofharvesting,thechiefproblemofthevisionsystemistorecognizeandlocatethefruitobject3.Here,recognitionmeanssegmentationofthefruitobjectsfromthecomplicatedbackground4.Andlocationincludestwoaspectslocationofthefruitcenterandabscissionpoint.786H.Guetal.Recently,thereremanyresearchesaboutfruitandvegetableharvestingrobotbasedonmachinevision56.CaiJianrongpresentedthemachinevisionrecognitionmethodsunderthenaturescene.UsingtheOtsualgorithm,itgotthesegmentationthresholdautomaticallyandextractedthetarget7.Miyanagaintroducedtheseedinggraftingtechniquebasedonmachinevisionandtherobotinventedbythemhasbeenputintoproduction8.SlaughterD.Csetuponeorangeclassiermodelbyusingthecolorfeatureinthechromaticdigitalimage9.Amongtheseresearches,therehavebeenmanymethodsofextractingthefruitsfromcomplicatednaturescene.Butthebasicconceptionisextractingthefruitobjectbyconvertingonecolormodeltoanotheronewhichiseasiertoprocessormuchmoresuitableforthecase.However,still,therearetwoproblemsremainunsettled1Howtodeterminetheabscissionpointwhenthefruitsgrowaskew2Howtodeterminethecenterandabscissionpointwhentherearesomanyfruitoverlappedeachotherthatitisimpossibletodetectthewholeedge.Ifbothoftheproblemsremainunsettled,itmeanstheharvestingmaybeafailure.And,whatismoreimportant,thereisonlyabout40ofthefruitandvegetableisvisibleintheorchard10,whichmeansabout60objectsarepartiallyoccludedorcompletelyoccluded.Generally,theagriculturalrobotsarefitwithfanssoastoblowtheleavescoveringthefruit.So,forthefruitoccludedcompletely,itmaybepartiallyresolvedinthisway.So,inthepaper,weonlydiscussedtheproblemofthefruitpartiallyoccluded,inparticular,thecasethatonefruitoverlapanotherone.Asawhole,theproblemwearetodiscussbelongstothelowlevelmachinevision,andisoneofthekeystepsinthemachinevision.2MethodologyUsedinthePaper2.1MainIdeaFromtheanalysisabove,weknew,inordertosegmentthefruitfromleavesandbranches,weshouldusecolormodelsuitscertainsituations.TheRGBcolormodelcommonlyusedisnotsuitablefortheorchardimages.BecauseinRGBcolorspace,thetricolorRGBnotonlyrepresentthehuevalue,butalsorepresentthebrightness.So,thechangeoftheoutwardilluminationmayaddthedifficultyoftherecognition,soRGBisundependableintheprocessofthesegmentation.Inordertomakeuseofthefruitsclusteringfeatureinhuespace,weneedtoseparatethehueandbrightnessinformation.WecanachievethisgoalbytransferringtheRGBtothemodelswhichseparatehueandbrightness.2.2ColorModelsWeusethreetypesofcolormodelsinthepaper.ThefirstoneisLCDluminanceandcolordifferencemodel.Therearefourcolorattributesinthismodel,includingbrightnessinformationY,colordifferenceofred,Cr,colordifferenceofgreenCg,colordifferenceofblueCb.ThetransformformulaisasfollowsRecognitionandLocationofFruitObjectsBasedonMachineVision787⎪⎪⎩⎪⎪⎨⎧−−−YBCYGCYRCBGRYbgr114.0587.0299.0.1Duringtheprocessofexperiment,wefoundthatthecolordifferenceofredoffruitismuchhigherthanthatofleavesorbranches,eventheunripefruit,suchasunripetomatothatwouldbereferredlater.SoweonlyhavetoconsideraboutthecolordifferenceofredCr.ThesecondmodelweusedisNormalizedRGB.Thediagramwasusedtorepresentthecolorpropertiesofthethreeportions.Thetransformformulaisdefinedasfollows⎪⎩⎪⎨⎧///BGRBbBGRGgBGRRr.2itisobviousitsatisfies1bgr.Combinedtheadvantagesoftheabovetwomodels,wecanconcludethethirdcolormodelcalledLHMinthispaper.ChoosingYandCrfromthefirstcolormodel,randgfromthesecondmodelwecanconstructtheformulaasfollow⎪⎪⎩⎪⎪⎨⎧−//114.0587.0299.0BGRGgBGRRrYRCBGRYr.33SegmentationUnderthenaturesceneoftheorchard,thefactorscontainingthenonuniformillumination,theocclusionoftheleafandbranchallmakeitmoredifficulttosegment.Atpresent,wecanclassifythechromaticimagesegmentationintothreeclasses1Segmentationbasedonthreshold2Segmentationbasedonedgeinspectingandareagrowing3Segmentationbasedoncolorclustering11.3.1ClusteringandClassifierTheprimaryconceptionofclusteringistodistinguishthedifferentobjectswhichincludedifferentclassesofobjectsanddifferentpartsofthesameobject12.Allclassificationalgorithmsarebasedontheassumptionthattheimageinquestiondepictsoneormorefeaturesandthateachofthesefeaturesbelongstooneofseveraldistinctandexclusiveclasses.Thetraditionalwayofclassifiercomprisestwophasesofprocesstrainingandtesting.Intheinitialtrainingphase,characteristicpropertiesoftypicalimagefeaturesareisolatedand,basedonthese,auniquedescriptionofeachclassificationcategory,i.e.trainingclass,iscreated.Inthesubsequenttestingphase,thesefeaturespacepartitionsareusedtoclassifyimagefeatures.788H.Guetal.Intheexperiment,wesampled60pixelsofleaf,branch,andfruitrespectivelyandconstructedaclassifier.Adoptingtwofeaturepatternsmandn,weformedthedecisionfunctionscbnamnmf,,wherea,b,andcarearbitraryconstantsaslongasthepointsonthelinesatisfiesthecondition0,nmf.Here,featurepatternmaybecolor,shape,size,oranypropertiesoftheobjects.Accordingtothedecisionfunctions0,nmfor0,nmf,wecandividetheimageintotwopartsasshowninFig1.Fig.1.Modelofclassifier3.2SegmentationoftheFruitObjectsInthisstudy,weadoptedthesegmentationmethodofseveralthresholds.Thethresholdsarederivedfromtheabovethreemodelsoftheimageusingthedecisionfunctions.Accordingtotheaboveparagraphs,wecouldgetthreedecisionfunctionsthefirstfunction,F1,separatedthefruitportionandtheleafportion,thesecondfunction,F2,separatedthefruitportionfromthebranchportion,andthethirdfunctionF3,separatedtheleafportionfromthebranchportion.But,onthebasisoftherequestoftheexperiment,weonlyhavetosegmentthefruitfromthebackground,andtheleafandbranchportionswereregardedasbackground.So,therewasnoneedtoconsiderF3.3.3AnalyzingtheImageUsingtheLCDModelItisobviousthatthefruit,leafandbranchhadthedifferentbrightnessandcolordifferenceofthered.So,sampled60pixelsofthefruit,leafandbranchtotrain,fromFig2a,weknewthedistancebetweenthemeanvaluesofthefruitobjectandthatofthebranchandleafwasrathergreat,soitwasappropriatetousetheminimumdistanceclassifier.Fromthetrainingset,wecouldgetthedecisionfunctionsaccordingtotheminimumdistanceclassifierasfollows
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