基于区域增长的立体匹配算法分析

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1、万方数据AbstractEyes，isthemainrouteofhumanperceptionfromcircumstances．Thecomputersnotonlyacquire，recognizeandgettheinformationfromthree·dimensionalscenebymodelingthehumanvisionsystems，butalsomakerelatesanalysis．ComputervisionCanworkinavarietyofdangeroussituationsinsteadofthehuman

2、eyes．Soresearchesonmachinevisionisofquitesignificance．StereomatchingisafocusandchMlengeincomputervision，andhowtoachievestereomatchingquicklywithhigh-precisionisthekeypointformanyscholarsinrecentyears．Becausestereomatchingalgorithmbasedonregiongrowinghasmanyadvantages，thispape

3、radoptsittodoappropriatestereomatchingforimages·Mainworkofthispaperareasfollow：1Illuminationvariationhasacertaininfluenceonthepixelgrayofimage．ThematchingerroroftraditionalCensusTransformalgorithmresultsfromtheover-relianceonthecentralpixel’Sgray．Sothispaperputsforwardanimpro

4、vedCensustransfoITnalgorithmwithhighrobust．Firstly,asumofgrayavqrageandlocalcontrastisappliedtoreplacethecenterpixel’Sgrayvalueintransformwindow,whichimprovethenoiseresistanceoftransformationresultsandthedifferencebetweendifferentblocks．Next，itintroducestheGaussiantemplateint

5、otlleCensustransformationtoenhantetheweightofpixelsclosedtocenterpixel，whicheffectivelyreducedtheimpactofdiscontinuousareaonthematching．Finally,itaddsthesimulationlightingtotheimagesofMiddleburyandemploystheactualmatchingvaluewhichisofferedbyMiddleburytocalculatethematchingpr

6、ecisioninthispaper．Experimentalresultsshowthatthealgorithmhasahighermatchingaccuracyandastrongerrobustnessthantraditionalalgorithm．21Thepaperhasstudiedthemechanismandtheexpansionstepsofregiongrowingalgorithm．Confrontedwithlowtextureimagebasedontraditionalregiongrowingmatching

7、algorithm，thegrowingCan’tbeofwiderangebecausetheseedpoints’numberisnotenough．SoamatchingalgorithmbasedonafusionofSIFTfeaturesandcontourinformationisputforward．Firstofall，theSIFTfeaturepointsandtheinformationofthecontourareextracted．Then，itusestwostepsofmatchingmethodtofinisht

8、hejobofmatchingcontourpoints：thefirststep，thefeaturepointsandepipola