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1、AutomaticPanamicImageStitchingusingInvariantFeaturesMatthewBrownDavidG.Lowembrown|lowe@cs.ubc.caDepartmentofComputerScienceUniversityofBritishColumbiaVancouverCanada.AbstractThispaperconcernstheproblemoffullyautomatedpan

2、amicimagestitching.Thoughthe1Dproblem(singleaxisofrotation)iswellstudied2Dmultirowstitchingismedifficult.Previousapproacheshaveusedhumaninputrestrictionsontheimagesequenceindertoestablishmatchingimages.Inthiswkwefmulates

3、titchingasamultiimagematchingproblemuseinvariantlocalfeaturestofindmatchesbetweenalloftheimages.Becauseofthisourmethodisinsensitivetothederingientationscaleilluminationoftheinputimages.Itisalsoinsensitivetonoiseimagestha

4、tarenotpartofapanamacanrecognisemultiplepanamasinanunderedimagedataset.Inadditiontoprovidingmedetailthispaperextendsourpreviouswkintheareabyintroducinggaincompensationautomaticstraighteningsteps.1.IntroductionPanamicimag

5、estitchinghasanextensiveresearchliteratureseveralcommercialapplications.Thebasicgeometryoftheproblemiswellunderstoodconsistsofestimatinga33cameramatrixhomographyfeachimage.Thisestimationprocessneedsaninitialisationwhichi

6、stypicallyprovidedbyuserinputtoapproximatelyaligntheimagesafixedimagedering.FexamplethePhotoStitchsoftwarebundledwithCanondigitalcamerasrequiresahizontalverticalsweepasquarematrixofimages.REALVIZStitcherversion4hasauseri

7、nterfacetoroughlypositiontheimageswithamousebefeautomaticregistrationproceeds.Ourwkisnovelinthatwerequirenosuchinitialisationtobeprovided.Intheresearchliteraturemethodsfautomaticimagealignmentstitchingfallbroadlyintotwoc

8、ategies—directfeaturebased.Directmethodshavetheadvantagethattheyusealloftheavailableimagedatahencecanprovideveryaccurateregistrationbuttheyrequireacloseinitialisation.Featurebasedregistrationdoesnotrequireinitialisationb

9、uttraditionalfeaturematchingmethods(e.g.crelationofimagepatchesaroundHarriscners)lacktheinvariancepropertiesneededtoenablereliablehleimageswithvaryingientationzoom(seefigure8).Notethatthiswouldnotbepossibleusingtradition

10、alfeaturematchingtechniquessuchascrelationofimagepatchesaroundHarriscners.dinary(translational)crelationisnotinvariantunderrotationHarriscnersarenotinvarianttochangesinscale.Assumingthatthecamerarotatesaboutitsopticalcen

11、trethegroupoftransfmationstheimagesmayundergoisaspecialgroupofhomographies.Weparameteriseeachcamerabyarotationvectfocallength.Thisgivespairwise??321????，，?fhomographieswherejijiuHu~~?(1)1??jTjiiijKRRKHarethehomogeneousim

12、agepositions(，whereisthe2jiuu~~??1~iiiusu?iudimensionalimageposition).The4parametercameramodelisdefinedby(2)???????????1000000iiiffK(usingtheexponentialrepresentationfrotations)(3)????ieRi??????????????????000121323iiiii

13、ii???????Ideallyonewoulduseimagefeaturesthatareinvariantunderthisgroupoftransfmations.Howeverfsmallchangesinimageposition(4)jujiiiuuuuui?????00equivalentlywherejijiuAu~~?(5)???????????100232221131211aaaaaaAijisanaffinetr

14、ansfmationobtainedbylinearisingthehomographyabout.Thisimplies0iuthateachsmallimagepatchundergoesanaffinetransfmationjustifiestheuseofSIFTfeatureswhicharepartiallyinvariantunderaffinechange.Oncefeatureshavebeenextractedfr

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