數(shù)字圖像處理課件10年

數(shù)字圖像處DigitalImage--Feature2010-06-數(shù)字圖像處DigitalImage--Feature2010-06-WhatisaLine:straightline,curve,WhatisaLine:straightline,curve,Edge:2D,3D,Shape:rectangle,circle,ellipse,sphere,WhatdowewanttoWhatdowewanttoWhyisitWhyisitWhatisalookingatthepointsthroughWhatisalookingatthepointsthroughasmallgivingalargechangeinintensitywhenshiftingawindowinanydirectionsWhatisaFlat,edgeand“flat”region:nochangeinall“edge”:nochangealongtheedge“corner”:significantchangeinallWhatisaFlat,edgeand“flat”region:nochangeinall“edge”:nochangealongtheedge“corner”:significantchangeinallCornerBasicforasmallshift(x,y),changeCornerBasicforasmallshift(x,y),changeofE(u,v)w(x,y)[I(xu,yv)I(x,x,wherewindowis1inthewindow,0w(x,oruseGaussianit’sacornerifforalltheshiftsEisveryProblemsofthisitisrelatedtox,itisrelatedtou,toomuchMoravec算Moravec于1977年提出利用灰度方差提取點Moravec算Moravec于1977年提出利用灰度方差提取點特征的算灰度方差的點作為特征點interestmeasureistheminimumofthesefoursumssquaresofdifferencesofpixelsadjacentineachoffourdirectionsFeaturesarechosenwheretheinterestmeasurehaslocalmaxima.ThefeatureisconceptuallythepointatthecenterofthewindowwiththislocallymaximalHarrisCornerSolvetheseIE[Ix, xu,HarrisCornerSolvetheseIE[Ix, xu,yvux,x,[uUvVO(u2v2x,x,UI/VI/HarrisCornerSolvethesesoE(u,v)AuHarrisCornerSolvethesesoE(u,v)Au22CuvwhereMisa2*2matrixcomputedfromimageCBHarrisCornerRelationshipanddirectionofHarrisCornerRelationshipanddirectionofHarrisCornerLet1,betheeigenvaluesofHarrisCornerLet1,betheeigenvaluesofif1and2aresmall,Eisalmostconstantinalldirections,it’sflatregionif1ismuchlargerthan2,or2ismuchlargerthan1,it’sanedge1~2Eincreasesindirections,it’saHarrisCornerMeasureofcornerHarrisCornerMeasureofcornerHarrisCornersoifRisverysmall,HarrisCornersoifRisverysmall,it’sifRisnegative,it’sanifRisverylarge,it’saTheFindpointswithlargecornerresponsefunctionR(R>TakethepointsoflocalmaximaofHarriscornerHarriscornerCategoriesofColorsimilarityBagCategoriesofColorsimilarityBagofOperatorofImage:Featurepoints/CornerImages:FeatureVideo:FeatureSpaceofImageScaleSpatialtemporalFeatureFeatureKanade-Lucas-Tomasi(KLT)FeatureTrackingGoodforvideoMainKanade-Lucas-Tomasi(KLT)FeatureTrackingGoodforvideoMainFeatureFeatureKanade-Lucas-Tomasi(KLT)FeatureProblemFindaKanade-Lucas-Tomasi(KLT)FeatureProblemFindafeature’sxx,Kanade-Lucas-Tomasi(KLT)FeatureImageFeatureHarriscornerTarget:FindKanade-Lucas-Tomasi(KLT)FeatureImageFeatureHarriscornerTarget:FindthedisplacementdofaKanade-Lucas-Tomasi(KLT)FeatureTracker-FeaturetrackingThedisplacementvectordisthenKanade-Lucas-Tomasi(KLT)FeatureTracker-FeaturetrackingThedisplacementvectordisthenchosensoastominimizetheresidueerrorLinearizeimagefunctionsbythetruncatedTaylorKanade-Lucas-Tomasi(KLT)FeatureTracker-FeaturetrackingRewritetheDifferentiatingthelastexpressionKanade-Lucas-Tomasi(KLT)FeatureTracker-FeaturetrackingRewritetheDifferentiatingthelastexpressionoftheresiduewithrespecttod:anddisassumedtobeconstantwithinKanade-Lucas-Tomasi(KLT)FeatureTracker-FeaturetrackingLinearIteratethisprocessuntilKanade-Lucas-Tomasi(KLT)FeatureTracker-FeaturetrackingLinearIteratethisprocessuntilKanade-Lucas-TomasiTheminoreigenvalueofOriginalTrackedKanade-Lucas-TomasiTheminoreigenvalueofOriginalTrackedResult:inputResult:inputResult:outputResult:outputKanade-Lucas-TomasiGoodFeaturestoTrack--AffinemotionKanade-Lucas-TomasiGoodFeaturestoTrack--AffinemotionKanade-Lucas-TomasiGoodFeaturestoKanade-Lucas-TomasiGoodFeaturestoKanade-Lucas-TomasiGoodFeaturestoKanade-Lucas-TomasiGoodFeaturestoCornerImagevariatesalongany00CornerImagevariatesalongany00AnedgeIv’slocalmaximumindicatesandI,isthegradientIv10AnedgeIv’slocalmaximumindicatesandI,isthegradientIv10Corner hasalocalIv 000Corner hasalocalIv 000ScaleInvariantFeatureMoravecScaleInvariantFeatureMoravecScaleInvariantFeatureHarrisScaleInvariantFeatureHarrisScaleSpaceOnlytwo1.DefineimagedomainasScaleSpaceOnlytwo1.DefineimagedomainasI(x,y)It’sScaleSpaceasLx,y,tGx,y,tIx,y2.Findlocalextremet22Lx,y,tTonyLindeberg1994:Scale-spacetheory:AbasictoolforanalysingstructuresatdifferentEdgeIvhasaIvvIEdgeIvhasaIvvIZero-crossingInterpolation+-EdgedetectoratfineCoarsefeatureEdgedetectoratfineCoarsefeatureMultipleScaleImageMultipleScaleImageEdgedetectoratcoarserFinefeatureEdgedetectoratcoarserFinefeatureScalespacex,y,tScalespacex,y,tx,y,tIAmeasureforextremeoverEffectofScaleInvariantEdgeDetectorEffectofScaleInvariantEdgeDetectorProblemFindaoneparameterfIProblemFindaoneparameterfIx,y,tLx,y,tSothatLx,y,tisequivalenttoascaledfromtheoriginalone,whilethedefinitiondomainremainsthesameRequirementforScaleCausality:non-enhancementoflocalSemi-groupRequirementforScaleCausality:non-enhancementoflocalSemi-groupLx,y,tffIx,y,t1,t2fIx,y,t1t2Non-enhancementUniquescalespaceLtGUniquescalespaceLtGtI2/2t2 Gx,tD/22t2WhyZero-crossingTonyLindeberg:FeatureDetectionwithAutomaticScaleZero-crossingTonyLindeberg:FeatureDetectionwithAutomaticScaleBloblike3D1/2exp(-1/2x2-1/20424200Bloblike3D1/2exp(-1/2x2-1/20424200yxFindkeypointOnlytwo1.DefineimagedomainasFindkeypointOnlytwo1.DefineimagedomainasI(x,y)It’sScaleSpaceasLx,y,tGx,y,tIx,y2.Findlocalextremet22Lx,y,tt:ScalenormalizationNon-enhancement1.Localmaximumwillnot2.LocalNon-enhancement1.Localmaximumwillnot2.LocalminimumwillnotWhichmeansderivativeswillSignalIntensityofLaplaciandecreasesatx2(1+1/3sin(101012345xPlus,thresholdisSignalIntensityofLaplaciandecreasesatx2(1+1/3sin(101012345xPlus,thresholdisComputationalDiffusionequationt2Lt22ComputationalDiffusionequationt2Lt22LtLtLx,ktLtco