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1、1Fasterandbetter:amachinelearningapproachtocornerdetectionEdwardRosten,ReidPorter,andTomDrummondarXiv:0810.2434v1[cs.CV]14Oct2008EdwardRostenandReidPorterarewithLosAlamosNationalLaboratory,LosAlamos,NewMexico,USA,87544.Email:edrosten@lanl.gov,rporter@lanl.govTomDrummondis
2、withCambridgeUniversity,CambridgeUniversityEngineeringDepartment,TrumpingtonStreet,Cam-bridge,UK,CB21PZEmail:twd20@cam.ac.ukOctober14,2008DRAFT2AbstractTherepeatabilityandefficiencyofacornerdetectordetermineshowlikelyitistobeusefulinareal-worldapplication.Therepeatabilityi
3、simportandbecausethesamesceneviewedfromdifferentpositionsshouldyieldfeatureswhichcorrespondtothesamereal-world3Dlocations[1].Theefficiencyisimportantbecausethisdetermineswhetherthedetectorcombinedwithfurtherprocessingcanoperateatframerate.Threeadvancesaredescribedinthispap
4、er.First,wepresentanewheuristicforfeaturedetection,andusingmachinelearningwederiveafeaturedetectorfromthiswhichcanfullyprocesslivePALvideousinglessthan5%oftheavailableprocessingtime.Bycomparison,mostotherdetectorscannotevenoperateatframerate(Harrisdetector115%,SIFT195%).S
5、econd,wegeneralizethedetector,allowingittobeoptimizedforrepeatability,withlittlelossofefficiency.Third,wecarryoutarigorouscomparisonofcornerdetectorsbasedontheaboverepeatabilitycriterionappliedto3Dscenes.Weshowthatdespitebeingprincipallyconstructedforspeed,onthesestringent
6、tests,ourheuristicdetectorsignificantlyoutperformsexistingfeaturedetectors.Finally,thecomparisondemonstratesthatusingmachinelearningproducessignificantimprovementsinrepeatability,yieldingadetectorthatisbothveryfastandveryhighquality.IndexTermsCornerdetection,featuredetectio
7、n.I.INTRODUCTIONCornerdetectionisusedasthefirststepofmanyvisiontaskssuchastracking,localisation,SLAM(simultaneouslocalisationandmapping),imagematchingandrecognition.Thisneedhasdriventhedevelopmentofalargenumberofcornerdetectors.However,despitethemassiveincreaseincomputingp
8、owersincetheinceptionofcornerdetectors,itisstilltruethatwhenprocessinglivevideostreamsatfullfram