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基于随机森林和多特征融合的青苹果图像分割.docx

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基于随机森林和多特征融合的青苹果图像分割 Title:AppleImageSegmentationUsingRandomForestandMulti-FeatureFusion Abstract: Imagesegmentationplaysacrucialroleincomputervisionapplications,especiallyinagriculturalandfruitrecognitionsystems.Inthispaper,weproposeamethodforsegmentingappleimagesusingacombinationofrandomforestandmulti-featurefusion.Theproposedapproachaimstoaccuratelyidentifyandseparatepixelsbelongingtotheapplefromthebackground,ensuringpreciseandefficientfruitrecognition. 1.Introduction: Thedemandforautomatedagriculturalsystemshasincreasedinrecentyears,withafocusonimprovingcropyieldandquality.Oneessentialtaskinthesesystemsisfruitrecognition,whichheavilyreliesonaccurateimagesegmentation.Appleimagesegmentation,inparticular,posesuniquechallengesduetovariationsinshape,color,size,andlightingconditions.Therefore,aneffectivesegmentationalgorithmisrequiredtoidentifyandextractappleregionsaccurately. 2.RelatedWork: Severalsegmentationmethodshavebeenproposedinliterature,includingthresholding,region-basedsegmentation,andmachinelearning-basedapproaches.However,thesetechniquesoftenfailtoprovidesatisfactoryresultsduetothecomplexnatureofappleimages.Toaddresstheselimitations,ourmethodcombinesthestrengthsofrandomforestandmulti-featurefusion. 3.ProposedMethodology: 3.1ImagePreprocessing: Theproposedmethodologystartswithimagepreprocessingtechniquestoenhancethequalityoftheappleimages.Preprocessingstepslikenoiseremoval,contrastenhancement,andcolorspaceconversionareemployedtoimprovetheoverallaccuracyofsegmentation. 3.2Multi-FeatureExtraction: Toleveragethedistinctivecharacteristicsofappleimages,multiplefeaturessuchascolor,texture,andshapeareextracted.Colorfeaturesareobtainedfromvariouscolorspaces,includingRGB,HSV,andCIELAB.Texturefeaturesareextractedusingwell-knowntechniquessuchaslocalbinarypatterns(LBP)orGaborfilters.Shapeinformation,includingcircularityandcompactness,isalsoincorporated. 3.3RandomForestClassifier: Randomforestisawidelyusedalgorithmforclassificationtasksduetoitsrobustnessandabilitytohand