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1、PatternRecognitionLetters31(2010)1825–1832ContentslistsavailableatScienceDirectPatternRecognitionLettersjournalhomepage:www.elsevier.com/locate/patrecAnovelimagetemplatematchingbasedonparticlefilteringoptimizationa,*a,baHaoLi,Hai-BinDuan,Xiang-YinZhangaDepartmentofA
2、utomaticControl,BeijingUniversityofAeronauticsandAstronautics,Beijing,100083,PRChinabProvincialKeyLaboratoryforInformationProcessingTechnology,SuzhouUniversity,Suzhou215006,PRChinaarticleinfoabstractArticlehistory:Imagetemplatematchingisabasicandcrucialprocessforim
3、ageprocessing.Inthispaper,weproposeaAvailableonline11December2009newparticlefilteringoptimizationapproachtoimagetemplatematching.Theproposedmethodcom-binesthesumofsquareddifferences(SSD)orzeromeansumofabsolutedifferences(ZSAD)withnormal-Keywords:izedcrosscorrelation
4、(NCC)byGaussians.Astothecomputationcost,thefusion-basedapproachisratherImageprocessingslow.Theparticlefilterandchaosareutilizedtocalculateaportionofthesearchingfield.Inthisway,theTemplatematchingcomputationalcostcanbereduced.Inordertocomparativelyassesstheeffectivene
5、ssoftheproposedChaosapproachwithrespecttocompetingstate-of-arttechniquesfordifferentmatchingtasks,seriesofexper-Particlefilterimentsonavarietyofimageshavebeenconducted.Theexperimentalresultsshowthesuperiorperfor-Normalizedcrosscorrelation(NCC)manceoftheproposedmetho
6、dovertheconventionalfull-searchmethodwithrespecttovariationsuchasilluminationchanges,noisyenvironments.Ó2009ElsevierB.V.Allrightsreserved.1.IntroductionbustthanSADandSSDunderuniformilluminationchanges,ithasbeenwidelyusedinobjectrecognitionandindustrialinspec-Templa
7、tematchingisafundamentalprobleminpatternrecog-tion.Inourproposedmethod,SSDandNCCarechosenasthefu-nition,whichhasawideapplicationinobjectdetectionandobjectsioninformationtoimprovetherobustnessoftemplatetracking(YazdiandHosseini,2008).Templatematchingtech-matching.Th
8、efused-measurefunctioncanbedefinedbyðSSD=NCCÞniquesinvolvethetranslationofthetemplatetoeverypossiblepo-F¼pffiffiffiffi1e2d2,wheretheparameterdisrelatedto
