已阅读5页,还剩6页未读, 继续免费阅读
版权说明:本文档由用户提供并上传,收益归属内容提供方,若内容存在侵权,请进行举报或认领
文档简介
Z.Panetal.(Eds.):ICAT2006,LNCS4282,pp.785795,2006.Springer-VerlagBerlinHeidelberg2006RecognitionandLocationofFruitObjectsBasedonMachineVisionHuiGu,YayaLu,JilinLou,andWeitongZhangInformationEngineeringCollege,ZhejiangUniversityofTechnology,310014,Hangzhou,C,oo327,phonixlou,Abstract.Thispaperdiscussedthelowlevelmachinevisiononfruitandvegetableharvestingrobot,introducedtherecognitionandlocationoffruitandvegetableobjectsundernaturescenes,putforwardanewsegmentationmethodcombinedwithseveralcolormodels.Whatsmore,itpresentedanovelconceptionforthedeterminationoftheabscissionpoint,successfullyresolvedthelocationofcenterandabscissionpointwhenthefruitwerepartiallyoccluded.Meanwhile,bythetechniqueofgeometry,itsettledthelocationsoftheabscissionpointwhenthefruitgrewaskew.Itprovedgoodeffectunderthenaturescene.Keywords:Machinevision,fruitobject,recognition,location.1IntroductionDuringtheprocessofhumanconqueringtheNature,rebuildingtheNatureandpromotingthesociety,humansarefacingtheproblemofabilitylimitation.Asaresult,humanshavebeenseekingfortherobotstosubstitutethemantocompletecomplicatedtasks,andtheintelligentrobotisthebestchoice.Asweallknow,visionisthemainwayofhumansapperceivingtheworld.About80%informationisgotthroughvision.So,itisvitaltograntvisionfunctionforintelligentrobots.Here,wecandefinethemachinevisionasfollows:itisabletoproducesomedescriptionaboutthecontentoftheimageafterprocessingtheinputimage1.Therearemanyfieldsrelatedwithmachinevision.So,italsohasawideapplicationinvariousaspects,frommedicalimagetoremotesensedimage,fromindustrialinspectiontoagriculturalareas,etc.Thefruitandvegetableharvestingrobotwhichwearegoingtodiscussisonekindofautomaticmechanicalharvestingsystemspossessingtheperceptiveability,canbeprogrammedtoharvest,transferandpackthecrops2.Duringtheprocessofharvesting,thechiefproblemofthevisionsystemistorecognizeandlocatethefruitobject3.Here,recognitionmeanssegmentationofthefruitobjectsfromthecomplicatedbackground4.Andlocationincludestwoaspects:locationofthefruitcenterandabscissionpoint.786H.Guetal.Recently,thereremanyresearchesaboutfruitandvegetableharvestingrobotbasedonmachinevision56.CaiJian-rongpresentedthemachinevisionrecognitionmethodsunderthenaturescene.UsingtheOtsualgorithm,itgotthesegmentationthresholdautomaticallyandextractedthetarget7.Miyanagaintroducedtheseedinggraftingtechniquebasedonmachinevisionandtherobotinventedbythemhasbeenputintoproduction8.SlaughterD.Csetuponeorangeclassiermodelbyusingthecolorfeatureinthechromaticdigitalimage9.Amongtheseresearches,therehavebeenmanymethodsofextractingthefruitsfromcomplicatednaturescene.Butthebasicconceptionisextractingthefruitobjectbyconvertingonecolormodeltoanotheronewhichiseasiertoprocessormuchmoresuitableforthecase.However,still,therearetwoproblemsremainunsettled:1)Howtodeterminetheabscissionpointwhenthefruitsgrowaskew;2)Howtodeterminethecenterandabscissionpointwhentherearesomanyfruitoverlappedeachotherthatitisimpossibletodetectthewholeedge.Ifbothoftheproblemsremainunsettled,itmeanstheharvestingmaybeafailure.And,whatismoreimportant,thereisonlyabout40%ofthefruitandvegetableisvisibleintheorchard10,whichmeansabout60%objectsarepartiallyoccludedorcompletelyoccluded.Generally,theagriculturalrobotsarefitwithfanssoastoblowtheleavescoveringthefruit.So,forthefruitoccludedcompletely,itmaybepartiallyresolvedinthisway.So,inthepaper,weonlydiscussedtheproblemofthefruitpartiallyoccluded,inparticular,thecasethatonefruitoverlapanotherone.Asawhole,theproblemwearetodiscussbelongstothelowlevelmachinevision,andisoneofthekeystepsinthemachinevision.2MethodologyUsedinthePaper2.1MainIdeaFromtheanalysisabove,weknew,inordertosegmentthefruitfromleavesandbranches,weshouldusecolormodelsuitscertainsituations.TheRGBcolormodelcommonlyusedisnotsuitablefortheorchardimages.BecauseinRGBcolorspace,thetricolor(RGB)notonlyrepresentthehuevalue,butalsorepresentthebrightness.So,thechangeoftheoutwardilluminationmayaddthedifficultyoftherecognition,soRGBisundependableintheprocessofthesegmentation.Inordertomakeuseofthefruitsclusteringfeatureinhuespace,weneedtoseparatethehueandbrightnessinformation.WecanachievethisgoalbytransferringtheRGBtothemodelswhichseparatehueandbrightness.2.2ColorModelsWeusethreetypesofcolormodelsinthepaper.ThefirstoneisLCD(luminanceandcolordifference)model.Therearefourcolorattributesinthismodel,includingbrightnessinformationY,colordifferenceofred,Cr,colordifferenceofgreenCg,colordifferenceofblueCb.Thetransformformulaisasfollows:RecognitionandLocationofFruitObjectsBasedonMachineVision787=+=YBCYGCYRCBGRYbgr114.0587.0299.0.(1)Duringtheprocessofexperiment,wefoundthatthecolordifferenceofredoffruitismuchhigherthanthatofleavesorbranches,eventheunripefruit,suchasunripetomatothatwouldbereferredlater.SoweonlyhavetoconsideraboutthecolordifferenceofredCr.ThesecondmodelweusedisNormalizedRGB.Thediagramwasusedtorepresentthecolorpropertiesofthethreeportions.Thetransformformulaisdefinedasfollows:+=+=+=)/()/()/(BGRBbBGRGgBGRRr.(2)itisobviousitsatisfies:1=+bgr.Combinedtheadvantagesoftheabovetwomodels,wecanconcludethethirdcolormodelcalledLHMinthispaper.ChoosingYandCrfromthefirstcolormodel,randgfromthesecondmodel;wecanconstructtheformulaasfollow:+=+=+=)/()/(114.0587.0299.0BGRGgBGRRrYRCBGRYr.(3)3SegmentationUnderthenaturesceneoftheorchard,thefactorscontainingthenon-uniformillumination,theocclusionoftheleafandbranchallmakeitmoredifficulttosegment.Atpresent,wecanclassifythechromaticimagesegmentationintothreeclasses:(1)Segmentationbasedonthreshold;(2)Segmentationbasedonedgeinspectingandareagrowing;(3)Segmentationbasedoncolorclustering11.3.1ClusteringandClassifierTheprimaryconceptionofclusteringistodistinguishthedifferentobjectswhichincludedifferentclassesofobjectsanddifferentpartsofthesameobject12.Allclassificationalgorithmsarebasedontheassumptionthattheimageinquestiondepictsoneormorefeaturesandthateachofthesefeaturesbelongstooneofseveraldistinctandexclusiveclasses.Thetraditionalwayofclassifiercomprisestwophasesofprocess:trainingandtesting.Intheinitialtrainingphase,characteristicpropertiesoftypicalimagefeaturesareisolatedand,basedonthese,auniquedescriptionofeachclassificationcategory,i.e.trainingclass,iscreated.Inthesubsequenttestingphase,thesefeature-spacepartitionsareusedtoclassifyimagefeatures.788H.Guetal.Intheexperiment,wesampled60pixelsofleaf,branch,andfruitrespectivelyandconstructedaclassifier.Adoptingtwofeaturepatternsmandn,weformedthedecisionfunctions:cbnamnmf+=),(,wherea,b,andcarearbitraryconstantsaslongasthepointsonthelinesatisfiesthecondition0),(=nmf.Here,featurepatternmaybecolor,shape,size,oranypropertiesoftheobjects.Accordingtothedecisionfunctions0),(nmfor0),(nmf,wecandividetheimageintotwopartsasshowninFig1:.Fig.1.Modelofclassifier3.2SegmentationoftheFruitObjectsInthisstudy,weadoptedthesegmentationmethodofseveralthresholds.Thethresholdsarederivedfromtheabovethreemodelsoftheimageusingthedecisionfunctions.Accordingtotheaboveparagraphs,wecouldgetthreedecisionfunctions:thefirstfunction,F1
温馨提示
- 1. 本站所有资源如无特殊说明,都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
- 2. 本站的文档不包含任何第三方提供的附件图纸等,如果需要附件,请联系上传者。文件的所有权益归上传用户所有。
- 3. 本站RAR压缩包中若带图纸,网页内容里面会有图纸预览,若没有图纸预览就没有图纸。
- 4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
- 5. 人人文库网仅提供信息存储空间,仅对用户上传内容的表现方式做保护处理,对用户上传分享的文档内容本身不做任何修改或编辑,并不能对任何下载内容负责。
- 6. 下载文件中如有侵权或不适当内容,请与我们联系,我们立即纠正。
- 7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
最新文档
- 通风与空调综合项目工程综合项目施工专项方案
- 文化创意服务买断协议书
- 天然气开采用地地基转让合同
- 学生游泳安全协议
- 特殊路基处理综合项目施工专项方案
- 综合项目工程部经理岗位职能职责
- 雨季综合项目施工安全专项综合项目施工专项方案
- 企业相亲会活动专项方案
- 差旅费管理规定 (18篇)
- 山东建筑大学建筑城规学院《344风景园林基础》(建)专业硕士历年考研真题汇编合集
- 蒙自源饮食有限公司餐厅经理人事管理工作指引
- 苏教版七年级下册数学《期中检测题》附答案解析
- 配电网运行规程.doc
- 受限空间施工安全专项方案
- 工程洽商记录(最新整理)
- 人事前台文员——职位说明书(最新整理)
- 幼儿园家长委员会章程最新版本
- “三会一课”会议流程
- 二字词语接龙
- 戴尔授权书模板
- 常用磁芯规格参数
评论
0/150
提交评论