基于特征匹配的算法VIP免费

基于特征的匹配基于特征的匹配一一..特征匹配过程特征匹配过程11特征提取特征提取22特征描述特征描述33特征匹配特征匹配二二.SIFT.SIFT算法算法LocalfeaturesDetection:LocalfeaturesDetection:2x2matrixofimagederivatives(averagedinneighborhoodofapoint).（1）平移Translation（2）欧几里德几何（平移+旋转）（3）相似性变换（平移+旋转+尺度）（4）仿射变换（5）投影变换Theneedforinvariance1.几何变换2.光照变化一一..特征匹配过程特征匹配过程11特征提取特征提取1.1HarrisandHessianDetector1.1HarrisandHessianDetector1.21.2尺度不变特征检测尺度不变特征检测1.31.3仿射不变特征检测仿射不变特征检测1.41.4特征提取总结特征提取总结22特征描述特征描述33特征匹配特征匹配二二.SIFT.SIFT算法算法（（11））Harrisdetector(Harris,19Harrisdetector(Harris,1988)88)Secondmomentmatrix/autocorrelationSecondmomentmatrix/autocorrelationmatrixmatrix),(),(),(),(),(),()(),,(),,(222HarrisDyDxDxDxDxDxIDDIDIIIIIIIGxxxxxxxxM2222221)()(*)(),(yxDDxegxIgxxI公式由来说明影像信号的局部自相关函数影像信号的局部自相关函数WyxwwwwywwwwxwwwwyyxxIyxIyyxxIyxIyxf),(22)],(),([)],(),([),(yxyxIyxIyxIyyxxIwwywwxwwww),(),(),(),(给定点给定点(x,y)(x,y)及位移及位移(△x,△y)(△x,△y)，窗口为，窗口为WW，用差平方和，用差平方和(SSD)(SSD)近似自相关函数，近似自相关函数，计算窗口计算窗口WW和位移窗口内灰度的差别。和位移窗口内灰度的差别。位移后影像函数通过一阶泰勒展开式近似重新计算f(x,y):yxyxyxyxIyxIyxIyxIyxyxyxIyxIyxfwwywwxWyxwwywwxWyxwwywwxwwwwM),(),(),(),(]),(),([),(),(),(2“secondmomentmatrixM”Autocorrelation(Autocorrelation(secondsecondmoment)moment)matrixmatrix21),(2),(),(),(200),(),(),(),(),(),(WyxwwyWyxwwywwxWyxwwywwxWyxwwxwwwwwwwwyxIyxIyxIyxIyxIyxIM—Mcanbeusedtoderiveameasureof“cornerness”—Independentofvariousdisplacements(Δx,Δy)—Corner:significantgradientsin>1directionsrankM=2—Edge:significantgradientin1directionrankM=1—HomogeneousregionrankM=0HarrisdetectorHarrisdetector流程流程1.Imagederivatives1.Imagederivatives2.Squareofderivatives2.Squareofderivatives3.Gaussianfilterg()3.Gaussianfilterg()4.Cornernessfunction4.Cornernessfunction5.Non-maximasuppressio5.Non-maximasuppressionnccHarris>Harris>ttHarrisHarrisIMMtracedetHarrisc（（22））Hessiandetector(Beaudet,197Hessiandetector(Beaudet,1978)8)TaylorTaylor二阶展开式二阶展开式xxxxIxxIxxITT)()()()(0000得到Hessian矩阵(,)(,)(,)(,)xxDxyDxyDyyDxxIxIxHIxIx其中，I是高斯平滑函数的二阶偏导数2)det(xyyyxxIIIHIIxxIxyIyy小总结小总结HarrisdetectorHarrisdetector◇◇Rotationinvariant?YesRotationinvariant?YesTheeigenvaluesofMrevealtheamountofintensitychangeinthetwoprincipalorthogonalgradientdirectionsinthewindow.◇◇Scaleinvariant?NoScaleinvariant?NoHessiandetectorHessiandetectorTXXM2100◇◇Rotationinvariant?Rotationinvariant?YesYes◇◇Scaleinvariant?NoScaleinvariant?No一一..特征匹配过程特征匹配过程11特征提取特征提取1.1HarrisandHessianDetector1.1HarrisandHessianDetector1.21.2尺度不变特征检测尺度不变特征检测1.31.3仿射不变特征检测仿射不变特征检测1.41.4特征提取总结特征提取总结22特征描述特征描述33特征匹...