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2-D correlation coefficient

`r = corr2(A,B)`

r = corr2(gpuarrayA,gpuarrayB)

`r = corr2(A,B)`

returns the
correlation coefficient `r`

between `A`

and `B`

,
where `A`

and `B`

are matrices or
vectors of the same size. `r`

is a scalar `double`

.

`r = corr2(gpuarrayA,gpuarrayB)`

performs
the operation on a GPU. The input images are 2-D gpuArrays of the
same size. `r`

is a scalar `double`

gpuArray.
This syntax requires the Parallel
Computing Toolbox™.

`A`

and `B`

can be numeric
or logical. The return value `r`

is a scalar `double`

.

`gpuarrayA`

and `gpuarrayB`

must
be real, 2-D gpuArrays. If either `A`

or `B`

is
not a gpuArray, it must be numeric or logical and nonsparse. `corr2`

moves
any data not already on the GPU to the GPU. `R`

is
a scalar double gpuArray.

Compute the correlation coefficient between an image and the same image processed with a median filter.

```
I = imread('pout.tif');
J = medfilt2(I);
R = corr2(I,J)
```

R = 0.9959

Compute the correlation coefficient on a GPU between an image and the same image processed using standard deviation filtering.

```
I = gpuArray(imread('pout.tif'));
J = stdfilt(I);
R = corr2(I,J)
```

R = 0.2762

`corr2`

computes the correlation coefficient
using

$$r=\frac{{\displaystyle \sum _{m}{\displaystyle \sum _{n}({A}_{mn}-\overline{A})({B}_{mn}-\overline{B})}}}{\sqrt{\left({\displaystyle \sum _{m}{\displaystyle \sum _{n}{\left({A}_{mn}-\overline{A}\right)}^{2}}}\right)\left({\displaystyle \sum _{m}{\displaystyle \sum _{n}{\left({B}_{mn}-\overline{B}\right)}^{2}}}\right)}}$$

where $$\overline{A}$$ `=`

`mean2(A)`

,
and $$\overline{B}$$ `=`

`mean2(B)`

.

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