外文翻译基于机器视觉的水果的识别和定位.pdf外文翻译基于机器视觉的水果的识别和定位.pdf

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ZPANETALEDSICAT2006,LNCS4282,PP785–795,2006SPRINGERVERLAGBERLINHEIDELBERG2006RECOGNITIONANDLOCATIONOFFRUITOBJECTSBASEDONMACHINEVISIONHUIGU,YAYALU,JILINLOU,ANDWEITONGZHANGINFORMATIONENGINEERINGCOLLEGE,ZHEJIANGUNIVERSITYOFTECHNOLOGY,310014,HANGZHOU,CHINAGHZJUTEDUCN,{OO327,PHONIXLOU,SEASONZWT}163COMABSTRACTTHISPAPERDISCUSSEDTHELOWLEVELMACHINEVISIONONFRUITANDVEGETABLEHARVESTINGROBOT,INTRODUCEDTHERECOGNITIONANDLOCATIONOFFRUITANDVEGETABLEOBJECTSUNDERNATURESCENES,PUTFORWARDANEWSEGMENTATIONMETHODCOMBINEDWITHSEVERALCOLORMODELSWHAT’SMORE,ITPRESENTEDANOVELCONCEPTIONFORTHEDETERMINATIONOFTHEABSCISSIONPOINT,SUCCESSFULLYRESOLVEDTHELOCATIONOFCENTERANDABSCISSIONPOINTWHENTHEFRUITWEREPARTIALLYOCCLUDEDMEANWHILE,BYTHETECHNIQUEOFGEOMETRY,ITSETTLEDTHELOCATIONSOFTHEABSCISSIONPOINTWHENTHEFRUITGREWASKEWITPROVEDGOODEFFECTUNDERTHENATURESCENEKEYWORDSMACHINEVISION,FRUITOBJECT,RECOGNITION,LOCATION1INTRODUCTIONDURINGTHEPROCESSOFHUMANCONQUERINGTHENATURE,REBUILDINGTHENATUREANDPROMOTINGTHESOCIETY,HUMANSAREFACINGTHEPROBLEMOFABILITYLIMITATIONASARESULT,HUMANSHAVEBEENSEEKINGFORTHEROBOTSTOSUBSTITUTETHEMANTOCOMPLETECOMPLICATEDTASKS,ANDTHEINTELLIGENTROBOTISTHEBESTCHOICEASWEALLKNOW,VISIONISTHEMAINWAYOFHUMANSAPPERCEIVINGTHEWORLDABOUT80INFORMATIONISGOTTHROUGHVISIONSO,ITISVITALTOGRANTVISIONFUNCTIONFORINTELLIGENTROBOTSHERE,WECANDEFINETHEMACHINEVISIONASFOLLOWSITISABLETOPRODUCESOMEDESCRIPTIONABOUTTHECONTENTOFTHEIMAGEAFTERPROCESSINGTHEINPUTIMAGE1THEREAREMANYFIELDSRELATEDWITHMACHINEVISIONSO,ITALSOHASAWIDEAPPLICATIONINVARIOUSASPECTS,FROMMEDICALIMAGETOREMOTESENSEDIMAGE,FROMINDUSTRIALINSPECTIONTOAGRICULTURALAREAS,ETCTHEFRUITANDVEGETABLEHARVESTINGROBOTWHICHWEAREGOINGTODISCUSSISONEKINDOFAUTOMATICMECHANICALHARVESTINGSYSTEMSPOSSESSINGTHEPERCEPTIVEABILITY,CANBEPROGRAMMEDTOHARVEST,TRANSFERANDPACKTHECROPS2DURINGTHEPROCESSOFHARVESTING,THECHIEFPROBLEMOFTHEVISIONSYSTEMISTORECOGNIZEANDLOCATETHEFRUITOBJECT3HERE,RECOGNITIONMEANSSEGMENTATIONOFTHEFRUITOBJECTSFROMTHECOMPLICATEDBACKGROUND4ANDLOCATIONINCLUDESTWOASPECTSLOCATIONOFTHEFRUITCENTERANDABSCISSIONPOINT786HGUETALRECENTLY,THERE’REMANYRESEARCHESABOUTFRUITANDVEGETABLEHARVESTINGROBOTBASEDONMACHINEVISION56CAIJIANRONGPRESENTEDTHEMACHINEVISIONRECOGNITIONMETHODSUNDERTHENATURESCENEUSINGTHEOTSUALGORITHM,ITGOTTHESEGMENTATIONTHRESHOLDAUTOMATICALLYANDEXTRACTEDTHETARGET7MIYANAGAINTRODUCEDTHESEEDINGGRAFTINGTECHNIQUEBASEDONMACHINEVISIONANDTHEROBOTINVENTEDBYTHEMHASBEENPUTINTOPRODUCTION8SLAUGHTERDCSETUPONEORANGECLASSIERMODELBYUSINGTHECOLORFEATUREINTHECHROMATICDIGITALIMAGE9AMONGTHESERESEARCHES,THEREHAVEBEENMANYMETHODSOFEXTRACTINGTHEFRUITSFROMCOMPLICATEDNATURESCENEBUTTHEBASICCONCEPTIONISEXTRACTINGTHEFRUITOBJECTBYCONVERTINGONECOLORMODELTOANOTHERONEWHICHISEASIERTOPROCESSORMUCHMORESUITABLEFORTHECASEHOWEVER,STILL,THEREARETWOPROBLEMSREMAINUNSETTLED1HOWTODETERMINETHEABSCISSIONPOINTWHENTHEFRUITSGROWASKEW;2HOWTODETERMINETHECENTERANDABSCISSIONPOINTWHENTHEREARESOMANYFRUITOVERLAPPEDEACHOTHERTHATITISIMPOSSIBLETODETECTTHEWHOLEEDGEIFBOTHOFTHEPROBLEMSREMAINUNSETTLED,ITMEANSTHEHARVESTINGMAYBEAFAILUREAND,WHATISMOREIMPORTANT,THEREISONLYABOUT40OFTHEFRUITANDVEGETABLEISVISIBLEINTHEORCHARD10,WHICHMEANSABOUT60OBJECTSAREPARTIALLYOCCLUDEDORCOMPLETELYOCCLUDEDGENERALLY,THEAGRICULTURALROBOTSAREFITWITHFANSSOASTOBLOWTHELEAVESCOVERINGTHEFRUITSO,FORTHEFRUITOCCLUDEDCOMPLETELY,ITMAYBEPARTIALLYRESOLVEDINTHISWAYSO,INTHEPAPER,WEONLYDISCUSSEDTHEPROBLEMOFTHEFRUITPARTIALLYOCCLUDED,INPARTICULAR,THECASETHATONEFRUITOVERLAPANOTHERONEASAWHOLE,THEPROBLEMWEARETODISCUSSBELONGSTOTHELOWLEVELMACHINEVISION,ANDISONEOFTHEKEYSTEPSINTHEMACHINEVISION2METHODOLOGYUSEDINTHEPAPER21MAINIDEAFROMTHEANALYSISABOVE,WEKNEW,INORDERTOSEGMENTTHEFRUITFROMLEAVESANDBRANCHES,WESHOULDUSECOLORMODELSUITSCERTAINSITUATIONSTHERGBCOLORMODELCOMMONLYUSEDISNOTSUITABLEFORTHEORCHARDIMAGESBECAUSEINRGBCOLORSPACE,THETRICOLORRGBNOTONLYREPRESENTTHEHUEVALUE,BUTALSOREPRESENTTHEBRIGHTNESSSO,THECHANGEOFTHEOUTWARDILLUMINATIONMAYADDTHEDIFFICULTYOFTHERECOGNITION,SORGBISUNDEPENDABLEINTHEPROCESSOFTHESEGMENTATIONINORDERTOMAKEUSEOFTHEFRUIT’SCLUSTERINGFEATUREINHUESPACE,WENEEDTOSEPARATETHEHUEANDBRIGHTNESSINFORMATIONWECANACHIEVETHISGOALBYTRANSFERRINGTHERGBTOTHEMODELSWHICHSEPARATEHUEANDBRIGHTNESS22COLORMODELSWEUSETHREETYPESOFCOLORMODELSINTHEPAPERTHEFIRSTONEISLCDLUMINANCEANDCOLORDIFFERENCEMODELTHEREAREFOURCOLORATTRIBUTESINTHISMODEL,INCLUDINGBRIGHTNESSINFORMATIONY,COLORDIFFERENCEOFRED,CR,COLORDIFFERENCEOFGREENCG,COLORDIFFERENCEOFBLUECBTHETRANSFORMFORMULAISASFOLLOWSRECOGNITIONANDLOCATIONOFFRUITOBJECTSBASEDONMACHINEVISION787⎪⎪⎩⎪⎪⎨⎧−−−YBCYGCYRCBGRYBGR1140587029901DURINGTHEPROCESSOFEXPERIMENT,WEFOUNDTHATTHECOLORDIFFERENCEOFREDOFFRUITISMUCHHIGHERTHANTHATOFLEAVESORBRANCHES,EVENTHEUNRIPEFRUIT,SUCHASUNRIPETOMATOTHATWOULDBEREFERREDLATERSOWEONLYHAVETOCONSIDERABOUTTHECOLORDIFFERENCEOFREDCRTHESECONDMODELWEUSEDISNORMALIZEDRGBTHEDIAGRAMWASUSEDTOREPRESENTTHECOLORPROPERTIESOFTHETHREEPORTIONSTHETRANSFORMFORMULAISDEFINEDASFOLLOWS⎪⎩⎪⎨⎧///BGRBBBGRGGBGRRR2ITISOBVIOUSITSATISFIES1BGRCOMBINEDTHEADVANTAGESOFTHEABOVETWOMODELS,WECANCONCLUDETHETHIRDCOLORMODELCALLEDLHMINTHISP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