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一种基于梯度增强的立体匹配算法.docx

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一种基于梯度增强的立体匹配算法 Title:AGradientBoosting-basedStereoMatchingAlgorithm Abstract: Stereomatchingisafundamentaltaskincomputervision,aimingtoestablishcorrespondencesbetweencorrespondingpointsinapairofstereoimages.Accuratestereomatchingiscrucialforapplicationssuchas3Dreconstruction,sceneunderstanding,anddepthestimation.Inthispaper,weproposeanovelstereomatchingalgorithmbasedongradientboosting,whichleveragesthepowerofensemblelearningtoimprovetheaccuracyandrobustnessofthematchingresults. 1.Introduction Stereomatchingisachallengingproblemduetothepresenceofocclusions,texturelessregions,andlightingvariations.Traditionalstereomatchingalgorithmssufferfromlimitationsinhandlingthesedifficulties,leadingtoinaccurateorincompletedepthmaps.Inrecentyears,machinelearningtechniques,especiallydeeplearning,haveshownpromisingresultsinaddressingthesechallenges.However,deeplearningmodelsoftenrequireextensivedatasetsandcomputationallyexpensivetrainingprocedures.Inthispaper,weproposeagradientboosting-basedstereomatchingalgorithmthatcombinestheadvantagesofbothtraditionalcomputervisiontechniquesandmachinelearningmethods. 2.RelatedWork Thissectionprovidesanoverviewoftheexistingstereomatchingalgorithms,includingtraditionalmethods,deeplearning-basedapproaches,andensemblelearningtechniques.Wediscusstheirstrengths,weaknesses,andlimitations,highlightingtheneedforamorerobustandaccuratestereomatchingalgorithm. 3.Methodology 3.1DisparityPropagation Westartbyestimatinganinitialdisparitymapusingatraditionalstereomatchingmethod,suchasthesumofabsolutedifferences(SAD)ornormalizedcross-correlation(NCC).Thisinitialmapservesastheinputforthegradientboostingprocess. 3.2FeatureExtraction Next,weextractasetofdiscriminativefeaturesfromthestereoimagesandtheircorrespondingdisparitymaps.Thesefeaturesmayincludeintensity-basedfeatures,gradient-basedfeatures,ortexturedescriptors.Thesefeaturesproviderichinformationaboutthelocalcharacteristicsoftheimageregionsandhelpindistinguishingbetweencorrectandincorrectmatches. 3.3Gradient