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1、ImageAnalysisandComputerVisionLecture4!ImageEnhencementinSpatialFiltering(II)WeiqiangWangSchoolofInformationScienceandEngineering,GUCASSeptember19,2010W.Q.Wang(SISE,GUCAS)ImageAnalysisandComputerVisionSeptember19,20101/44Outline1HistogramProcessingandFunc
2、tionPlotting2SpatialFiltering3StandardSpatialFiltersinImageProcessingToolboxW.Q.Wang(SISE,GUCAS)ImageAnalysisandComputerVisionSeptember19,20102/44Outline1HistogramProcessingandFunctionPlotting2SpatialFiltering3StandardSpatialFiltersinImageProcessingToolbo
3、xW.Q.Wang(SISE,GUCAS)ImageAnalysisandComputerVisionSeptember19,20102/44Outline1HistogramProcessingandFunctionPlotting2SpatialFiltering3StandardSpatialFiltersinImageProcessingToolboxW.Q.Wang(SISE,GUCAS)ImageAnalysisandComputerVisionSeptember19,20102/44Hist
4、ogramProcessingandFunctionPlottingHistogramMatchingHistogrammatchingissimilartohistogramequalization,exceptthatinsteadoftryingtomaketheoutputimagehavea
athistogram,wewouldlikeittohaveahistogramofaspeciedshape.Considerforamomentcontinuouslevelsthatarenorm
5、alizedtotheinterval[0,1],andletrandzdenotetheintensitylevelsoftheinputandoutputimages.Theinputlevelshaveprobabilitydensityfunctionpr(r)andtheoutputlevelshavethespeciedprobabilitydensityfunctionpz(z).W.Q.Wang(SISE,GUCAS)ImageAnalysisandComputerVisionSepte
6、mber19,20103/44HistogramProcessingandFunctionPlottingHistogramMatchingWeknowfromthediscussionintheprevioussectionthatthetransformation:Zrs=T(r)=pr(w)dw0resultinaidealequalizedhistogramps(s).SupposenowwedeneavariablezwiththepropertyZzH(z)=pz(w)dw=s0Fromth
7、eprecedingtwoequations,itfollowsthatz=H 1(s)=H 1(T(r));WecanndT(r)fromtheinputimage(thisisthehistogramequalizationtransformation),soitfollowsthatwecanusetheprecedingequationtondthetransformedlevelszwhosePDFisthespeciedpz(z)aslongaswecanndH 1.W.Q.Wang(
8、SISE,GUCAS)ImageAnalysisandComputerVisionSeptember19,20104/44HistogramProcessingandFunctionPlottingHistogramMatchingThetoolboximplementshistogrammatchingusingthefollowingsyntaxinhisteq:g=histeq(f,hspec)wherefisthein