cc [ flag... ] file... lmlib [ library... ] #include <mlib.h> mlib_status mlib_ImageNormCrossCorrel_Fp(mlib_d64 *correl, const mlib_image *img1, const mlib_image *img2, const mlib_d64 *mean2, const mlib_d64 *sdev2);
The mlib_ImageNormCrossCorrel_Fp() function computes the normalized crosscorrelation coefficients between a pair of floatingpoint images, on a perchannel basis.
It uses the following equations:
w1 h1 SUM SUM (d1[x][y][i] * d2[x][y][i]) x=0 y=0 correl[i] =  s1[i] * s2[i] d1[x][y][i] = img1[x][y][i]  m1[i] d2[x][y][i] = img2[x][y][i]  m2[i] 1 w1 h1 m1[i] =  * SUM SUM img1[x][y][i] w*h x=0 y=0 1 w1 h1 m2[i] =  * SUM SUM img2[x][y][i] w*h x=0 y=0 w1 h1 s1[i] = sqrt{ SUM SUM (img1[x][y][i]  m1[i])**2 } x=0 y=0 w1 h1 s2[i] = sqrt{ SUM SUM (img2[x][y][i]  m2[i])**2 } x=0 y=0
where w and h are the width and height of the images, respectively; m1 and m2 are the mean arrays of the first and second images, respectively; s1 and s2 are the unnormalized standard deviation arrays of the first and second images, respectively.
In usual cases, the normalized crosscorrelation coefficient is in the range of [1.0, 1.0]. In the case of (s1[i] == 0) or (s2[i] == 0), where a constant image channel is involved, the normalized crosscorrelation coefficient is defined as follows:
#define signof(x) ((x > 0) ? 1 : ((x < 0) ? 1 : 0)) if ((s1[i] == 0.)  (s2[i] == 0.)) { if ((s1[i] == 0.) && (s2[i] == 0.)) { if (signof(m1[i]) == signof(m2[i]) { correl[i] = 1.0; } else { correl[i] = 1.0; } } else { correl[i] = 1.0; } }
The two images must have the same type, the same size, and the same number of channels. They can have 1, 2, 3 or 4 channels. They can be of type MLIB_FLOAT or MLIB_DOUBLE.
If (mean2 == NULL) or (sdev2 == NULL), then m2 and s2 are calculated in this function according to the formulas shown above. Otherwise, they are calculated as follows:
m2[i] = mean2[i]; s2[i] = sdev2[i] * sqrt(w*h);
where mean2 and sdev2 can be the output of mlib_ImageMean() and mlib_ImageStdDev(), respectively.
In some cases, the resulting coefficients of this function could be NaN, Inf, or Inf.
The function takes the following arguments:
correl
img1
img2
mean2
sdev2
The function returns MLIB_SUCCESS if successful. Otherwise it returns MLIB_FAILURE.
See attributes(5) for descriptions of the following attributes:

mlib_ImageAutoCorrel(3MLIB), mlib_ImageAutoCorrel_Fp(3MLIB), mlib_ImageCrossCorrel(3MLIB), mlib_ImageCrossCorrel_Fp(3MLIB), mlib_ImageNormCrossCorrel(3MLIB), attributes(5)