MinEigenVal output normalization depends from derivative kernels

[Lorenzo_C_1]

Hi,

while comparing the minimum eigenvalue computed for an image using ippiEigenValsVecs_8u32f_C1R / ippiEigenValsVecs_32f_C1R with the same feature evaluated from scratch with the given formulas, it seems that the output is being normalized by a factor that depends from the employed derivative kernel. In particular, results are normalized by 4080 (255 << 4) and 65280 (255 << 8) in case of Sobel 3x3 and Sobel 5x5, respectively. The normalization factor for Scharr 3x3 seems about 6661.23.... where does this values come from? Anybody can answer?

thanks

PS: i am using IPP 6.0

[Chao_Y_Intel]

IPP computer vision functionality corresponds to OpenCV one – so normalization is done by the the same way as in OpenCV (in order to be compatible with OpenCV), so Normalization is as the following ways ………… denom = 1.0f/(Ipp32f)((1<<(2*apertureSize-2))*255); if (kernType==ippKernelSobel) { ……………………. } else { // kernType==ippKernelScharr denom *= 0.6125f; ……………………………… } …………… calcValues (bufXXomp,bufYYomp,bufXYomp,step32f,pEigenVV,eigStep,roiSize,denom,sign)

[Lorenzo_C_1]

Many thanks, compatibility with OpenCV makes sense.