'\" te .\" Copyright (c) 2007, Sun Microsystems, Inc. All Rights Reserved .TH mlib_ImageNormCrossCorrel 3MLIB "2 Mar 2007" "SunOS 5.11" "mediaLib Library Functions" .SH NAME mlib_ImageNormCrossCorrel \- normalized cross correlation .SH SYNOPSIS .LP .nf cc [ \fIflag\fR... ] \fIfile\fR... \fB-lmlib\fR [ \fIlibrary\fR... ] #include \fBmlib_status\fR \fBmlib_ImageNormCrossCorrel\fR(\fBmlib_d64 *\fR\fIcorrel\fR, \fBconst mlib_image *\fR\fIimg1\fR, \fBconst mlib_image *\fR\fIimg2\fR, \fBconst mlib_d64 *\fR\fImean2\fR, \fBconst mlib_d64 *\fR\fIsdev2\fR); .fi .SH DESCRIPTION .sp .LP The \fBmlib_ImageNormCrossCorrel()\fR function computes the normalized cross-correlation coefficients between a pair of images, on a per-channel basis. .sp .LP It uses the following equations: .sp .in +2 .nf w-1 h-1 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 w-1 h-1 m1[i] = ----- * SUM SUM img1[x][y][i] w*h x=0 y=0 1 w-1 h-1 m2[i] = ----- * SUM SUM img2[x][y][i] w*h x=0 y=0 w-1 h-1 s1[i] = sqrt{ SUM SUM (img1[x][y][i] - m1[i])**2 } x=0 y=0 w-1 h-1 s2[i] = sqrt{ SUM SUM (img2[x][y][i] - m2[i])**2 } x=0 y=0 .fi .in -2 .sp .LP where \fBw\fR and \fBh\fR are the width and height of the images, respectively; \fBm1\fR and \fBm2\fR are the mean arrays of the first and second images, respectively; \fBs1\fR and \fBs2\fR are the un-normalized standard deviation arrays of the first and second images, respectively. .sp .LP In usual cases, the normalized cross-correlation coefficient is in the range of \fB[-1.0, 1.0]\fR. In the case of \fB(s1[i] == 0)\fR or \fB(s2[i] == 0)\fR, where a constant image channel is involved, the normalized cross-correlation coefficient is defined as follows: .sp .in +2 .nf #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; } } .fi .in -2 .sp .LP 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 \fBMLIB_BYTE\fR, \fBMLIB_SHORT\fR, \fBMLIB_USHORT\fR or \fBMLIB_INT\fR. .sp .LP If \fB(mean2 == NULL)\fR or \fB(sdev2 == NULL)\fR, then \fBm2\fR and \fBs2\fR are calculated in this function according to the formulas shown above. Otherwise, they are calculated as follows: .sp .in +2 .nf m2[i] = mean2[i]; s2[i] = sdev2[i] * sqrt(w*h); .fi .in -2 .sp .LP where \fBmean2\fR and \fBsdev2\fR can be the output of \fBmlib_ImageMean()\fR and \fBmlib_ImageStdDev()\fR, respectively. .SH PARAMETERS .sp .LP The function takes the following arguments: .sp .ne 2 .mk .na \fB\fIcorrel\fR\fR .ad .RS 10n .rt Pointer to normalized cross correlation array on a channel basis. The array must be the size of channels in the images. \fBcorrel[i]\fR contains the cross-correlation of channel \fBi\fR. .RE .sp .ne 2 .mk .na \fB\fIimg1\fR\fR .ad .RS 10n .rt Pointer to first image. .RE .sp .ne 2 .mk .na \fB\fIimg2\fR\fR .ad .RS 10n .rt Pointer to second image. .RE .sp .ne 2 .mk .na \fB\fImean2\fR\fR .ad .RS 10n .rt Pointer to the mean array of the second image. .RE .sp .ne 2 .mk .na \fB\fIsdev2\fR\fR .ad .RS 10n .rt Pointer to the standard deviation array of the second image. .RE .SH RETURN VALUES .sp .LP The function returns \fBMLIB_SUCCESS\fR if successful. Otherwise it returns \fBMLIB_FAILURE\fR. .SH ATTRIBUTES .sp .LP See \fBattributes\fR(5) for descriptions of the following attributes: .sp .sp .TS tab() box; cw(2.75i) |cw(2.75i) lw(2.75i) |lw(2.75i) . ATTRIBUTE TYPEATTRIBUTE VALUE _ Interface StabilityCommitted _ MT-LevelMT-Safe .TE .SH SEE ALSO .sp .LP \fBmlib_ImageAutoCorrel\fR(3MLIB), \fBmlib_ImageAutoCorrel_Fp\fR(3MLIB), \fBmlib_ImageCrossCorrel\fR(3MLIB), \fBmlib_ImageCrossCorrel_Fp\fR(3MLIB), \fBmlib_ImageNormCrossCorrel_Fp\fR(3MLIB), \fBattributes\fR(5)