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1、AutomaticPanoramicImageStitchingusingInvariantFeaturesMatthewBrownandDavidG.Lowe{mbrown
2、lowe}@cs.ubc.caDepartmentofComputerScience,UniversityofBritishColumbia,Vancouver,Canada.AbstractThispaperconcernstheproblemoffullyautomatedpanoramicimagestitching.Thoughthe1Dproblem(singleaxisofrotation)i
3、swellstudied,2Dormulti-rowstitchingismoredifficult.Previousapproacheshaveusedhumaninputorrestrictionsontheimagesequenceinordertoestablishmatchingimages.Inthiswork,weformulatestitchingasamulti-imagematchingproblem,anduseinvariantlocalfeaturestofindmatchesbetweenalloftheimages.Becauseofthisour
4、methodisinsensitivetotheordering,orientation,scaleandilluminationoftheinputimages.Itisalsoinsensitivetonoiseimagesthatarenotpartofapanorama,andcanrecognisemultiplepanoramasinanunorderedimagedataset.Inadditiontoprovidingmoredetail,thispaperextendsourpreviousworkintheareabyintroducinggaincompe
5、nsationandautomaticstraighteningsteps.1.IntroductionPanoramicimagestitchinghasanextensiveresearchliteratureandseveralcommercialapplications.Thebasicgeometryoftheproblemiswellunderstood,andconsistsofestimatinga3×3cameramatrixorhomographyforeachimage.Thisestimationprocessneedsaninitialisation,
6、whichistypicallyprovidedbyuserinputtoapproximatelyaligntheimages,orafixedimageordering.Forexample,thePhotoStitchsoftwarebundledwithCanondigitalcamerasrequiresahorizontalorverticalsweep,orasquarematrixofimages.REALVIZStitcherversion4hasauserinterfacetoroughlypositiontheimageswithamouse,before
7、automaticregistrationproceeds.Ourworkisnovelinthatwerequirenosuchinitialisationtobeprovided.Intheresearchliteraturemethodsforautomaticimagealignmentandstitchingfallbroadlyintotwocategories—directandfeaturebased.Directmethodshavetheadvantagethattheyusealloftheavailableimagedataandhencecanprov
8、ideveryaccurateregistration,buttheyrequireacloseinitialisation.Featurebasedregistrationdoesnotrequireinitialisation,buttraditionalfeaturematchingmethods(e.g.,correlationofimagepatchesaroundHarriscorners)lacktheinvariancepropertiesneededtoenablereli
