Learning OpenCV, 2nd Edition

with each channel being separately computed from the corresponding index from src and the one-

dimensional lookup table.

cv::magnitude()

void cv::magnitude(

cv::InputArray x,

cv::InputArray y,

cv::OutputArray dst

);

dst

i

= x

i

2

+ y

i

2

cv::magnitude() essentially computes the radial part of a Cartesian-to-polar conversion on a two-

dimensional vector field. In the case of cv::magnitude(), this vector field is expected to be in the

form of two separate single channel arrays. These two input array should have the same size. (If you have a

single two-channel array, cv::split() will give you separate channels.) Each element in dst is

computed from the corresponding elements of x and y as the Euclidian norm of the two (i.e., the square

root of the sum of the squares of the corresponding values).

cv::Mahalanobis()

cv::Size cv::mahalanobis(

cv::InputArray vec1,

cv::InputArray vec2,

cv::OutputArray icovar

);

cvMahalanobis() computes the value:

r

mahalonobis

=

!

x −

!

µ

( )

T

Σ

−1

!

x −

!

µ

( )

The Mahalanobis distance is defined as the vector distance measured between a point and the center of a

Gaussian distribution; it is computed using the inverse covariance of that distribution as a metric. See

Figure 3-2. Intuitively, this is analogous to the z-score in basic statistics, where the distance from the center

of a distribution ...