texture_analysis_using_gabor_filter

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1、TEXTUREANALYSISUSINGGABORFILTERSTextureTypes•DefinitionofTexture•TexturetypesSyntheticNaturalStochasticTextureDefinitionTexture:theregularrepetitionofanelementorpatternonasurface.•Purposeoftextureanalysis:–Toidentifydifferenttexturedandnon-texturedregionsinanimage.–Tocl

2、assify/segmentdifferenttextureregionsinanimage.–Toextractboundariesbetweenmajortextureregions.–Todescribethetexelunit.–3-DshapefromtextureRef:[Forsyth2003,Raghu95]TextureRegions&EdgesIITMadrasR1R2R1R2R3R4R3R4TexturedimageImagehistogramsVPLABInputImageFILTERINGSMOOTHINGC

3、LASSIFIERSegmentedImageFlow-chartofatypicalmethodoftextureclassificationSyntheticTextureImageHorizontalintensityFilteredprofileoutputNonlinearSmoothingtransformSegmentedimageProcessingofTexture-likeImages2-DGaborFilter1⎡−1x2y2⎤f(x,y,ω,θ,σx,σy)=exp(()+())+jω(xcosθ+ysinθ)⎢⎥2πσxσy⎣2

4、σxσy⎦AtypicalGaussianAtypicalGaborfilterwithfilterwithσ=30σ=30,ω=3.14andθ=45°Gaborfilterswithdifferentcombinationsofspatialwidthσ,frequencyωandorientationθ.2-DGaborfilter:1⎡−1x2y2⎤f(x,y,ω,θ,σx,σy)=exp(()+())+jω(xcosθ+ysinθ)2πσxσy⎢⎣2σxσy⎥⎦whereσisthespatialspreadωisthefr

5、equencyθistheorientation1-DGaborfilter:1−x2f(x,ω,σ)=exp(+jωx)22πσ2σ21-DGaussianfunction:1⎛−x⎞g(x)=exp⎜⎟⎜2⎟2πσ1⎝2σ⎠ProcessingofTexture-likeImages1-DGaborFilter1-DGaussianFilter21−x2f(x,ω,σ)=exp(+jωx)1⎛−x⎞22g(x)=exp⎜⎟2πσσ⎜2⎟2πσ1⎝2σ⎠EvenComponentOddComponent2222g(x,y)=Kexp

6、(−π(a(x−x)+b(y−y)))0θ0θexp(j(2πF(xcosω+ysinω)+P)000•K:ScalesthemagnitudeoftheGaussianenvelop.•(a,b):ScalethetwoaxisoftheGaussianenvelop.•θ:(Rotation)angleoftheGaussianenvelop.•(x,y):Locationofthepeakofthe00Gaussianenvelop.•(u,v):Spatialfrequenciesofthe00sinusoidalcarrierinCartesi

7、ancoordinates.Itcanalsobeexpressedinpolarcoordinatesas(F,ω).00g(x,y)=•P:Phaseofthesinusoidalcarrier.2222Kexp(−π(a(x−x)+b(y−y))+j(2π(ux+vy)+P)0θ0θ00Asymmetric2-DGaussianfunctionAsymmetricalGaussianof128×128pixels.Theparametersareasfollows:x=y=0;a=1/50pixels;b=1/40pixels;θ=−45deg.0

8、0TherealandimaginarypartsofacomplexGabor