xcorr2
2-D cross-correlation
Description
Examples
Output Matrix Size and Element Computation
Create two matrices, M1
and M2
.
M1 = [17 24 1 8 15; 23 5 7 14 16; 4 6 13 20 22; 10 12 19 21 3; 11 18 25 2 9]; M2 = [8 1 6; 3 5 7; 4 9 2];
M1
is 5-by-5 and M2
is 3-by-3, so their cross-correlation has size (5+3-1)-by-(5+3-1), or 7-by-7. In terms of lags, the resulting matrix is
As an example, compute the element (or C(3,5)
in MATLAB®, since M2
is 3-by-3). Line up the two matrices so their (1,1)
elements coincide. This placement corresponds to . To find , slide M2
two rows to the right.
Now M2
is on top of the matrix M1(1:3,3:5)
. Compute the element-by-element products and sum them. The answer should be
[r2,c2] = size(M2); CC = sum(sum(M1(0+(1:r2),2+(1:c2)).*M2))
CC = 585
Verify the result using xcorr2
.
D = xcorr2(M1,M2); DD = D(0+r2,2+c2)
DD = 585
Two-Dimensional Cross-Correlation of Arbitrary Complex Matrices
Given a matrix of size and a matrix of size , their two-dimensional cross-correlation, , is a matrix of size with elements
is the trace and the dagger denotes Hermitian conjugation. The matrices and have size and nonzero elements given by
and
Calling xcorr2
is equivalent to this procedure for general complex matrices of arbitrary size.
Create two complex matrices, of size and of size .
X = randn([7 22])+1j*randn([7 22]); H = randn([6 17])+1j*randn([6 17]); [M,N] = size(X); m = 1:M; n = 1:N; [P,Q] = size(H); p = 1:P; q = 1:Q;
Initialize and .
Xt = zeros([M+2*(P-1) N+2*(Q-1)]); Xt(m+P-1,n+Q-1) = X; C = zeros([M+P-1 N+Q-1]);
Compute the elements of by looping over and . Reset to zero at each step. Save time and memory by summing element products instead of multiplying and taking the trace.
for k = 1:M+P-1 for l = 1:N+Q-1 Hkl = zeros([M+2*(P-1) N+2*(Q-1)]); Hkl(p+k-1,q+l-1) = H; C(k,l) = sum(sum(Xt.*conj(Hkl))); end end max(max(abs(C-xcorr2(X,H))))
ans = 1.5139e-14
The answer coincides to machine precision with the output of xcorr2
.
Align Two Images Using Cross-Correlation
Use cross-correlation to find where a section of an image fits in the whole. Cross-correlation enables you to find the regions in which two signals most resemble each other. For two-dimensional signals, like images, use xcorr2
.
Load a black-and-white test image into the workspace. Display it with imagesc
.
load durer img = X; White = max(max(img)); imagesc(img) axis image off colormap gray title('Original')
Select a rectangular section of the image. Display the larger image with the section missing.
x = 435; X = 535; szx = x:X; y = 62; Y = 182; szy = y:Y; Sect = img(szx,szy); kimg = img; kimg(szx,szy) = White; kumg = White*ones(size(img)); kumg(szx,szy) = Sect; subplot(1,2,1) imagesc(kimg) axis image off colormap gray title('Image') subplot(1,2,2) imagesc(kumg) axis image off colormap gray title('Section')
Use xcorr2
to find where the small image fits in the larger image. Subtract the mean value so that there are roughly equal numbers of negative and positive values.
nimg = img-mean(mean(img)); nSec = nimg(szx,szy); crr = xcorr2(nimg,nSec);
The maximum of the cross-correlation corresponds to the estimated location of the lower-right corner of the section. Use ind2sub
to convert the one-dimensional location of the maximum to two-dimensional coordinates.
[ssr,snd] = max(crr(:)); [ij,ji] = ind2sub(size(crr),snd); figure plot(crr(:)) title('Cross-Correlation') hold on plot(snd,ssr,'or') hold off text(snd*1.05,ssr,'Maximum')
Place the smaller image inside the larger image. Rotate the smaller image to comply with the convention that MATLAB® uses to display images. Draw a rectangle around it.
img(ij:-1:ij-size(Sect,1)+1,ji:-1:ji-size(Sect,2)+1) = rot90(Sect,2); imagesc(img) axis image off colormap gray title('Reconstructed') hold on plot([y y Y Y y],[x X X x x],'r') hold off
Recovery of Template Shift with Cross-Correlation
Shift a template by a known amount and recover the shift using cross-correlation.
Create a template in an 11-by-11 matrix. Create a 22-by-22 matrix and shift the original template by 8 along the row dimension and 6 along the column dimension.
template = 0.2*ones(11);
template(6,3:9) = 0.6;
template(3:9,6) = 0.6;
offsetTemplate = 0.2*ones(22);
offset = [8 6];
offsetTemplate((1:size(template,1))+offset(1), ...
(1:size(template,2))+offset(2)) = template;
Plot the original and shifted templates.
imagesc(offsetTemplate) colormap gray hold on imagesc(template) axis equal
Cross-correlate the two matrices and find the maximum absolute value of the cross-correlation. Use the position of the maximum absolute value to determine the shift in the template. Check the result against the known shift.
cc = xcorr2(offsetTemplate,template); [max_cc, imax] = max(abs(cc(:))); [ypeak, xpeak] = ind2sub(size(cc),imax(1)); corr_offset = [(ypeak-size(template,1)) (xpeak-size(template,2))]; isequal(corr_offset,offset)
ans = logical
1
The shift obtained from the cross-correlation equals the known template shift in the row and column dimensions.
GPU Acceleration for Cross-Correlation Matrix Computation
This example requires Parallel Computing Toolbox™ software. Refer to GPU Computing Requirements (Parallel Computing Toolbox) to see what GPUs are supported.
Shift a template by a known amount and recover the shift using cross-correlation.
Create a template in an 11-by-11 matrix. Create a 22-by-22 matrix and shift the original template by 8 along the row dimension and 6 along the column dimension.
template = 0.2*ones(11);
template(6,3:9) = 0.6;
template(3:9,6) = 0.6;
offsetTemplate = 0.2*ones(22);
offset = [8 6];
offsetTemplate((1:size(template,1))+offset(1), ...
(1:size(template,2))+offset(2)) = template;
Put the original and shifted template matrices on your GPU using gpuArray
objects.
template = gpuArray(template); offsetTemplate = gpuArray(offsetTemplate);
Compute the cross-correlation on the GPU.
cc = xcorr2(offsetTemplate,template);
Return the result to the MATLAB® workspace using gather
. Use the maximum absolute value of the cross-correlation to determine the shift, and compare the result with the known shift.
cc = gather(cc); [max_cc,imax] = max(abs(cc(:))); [ypeak,xpeak] = ind2sub(size(cc),imax(1)); corr_offset = [(ypeak-size(template,1)) (xpeak-size(template,2))]; isequal(corr_offset,offset)
ans = logical
1
Input Arguments
a
, b
— Input arrays
matrices
Input arrays, specified as matrices.
Example: sin(2*pi*(0:9)'/10)*sin(2*pi*(0:13)/20)
specifies a two-dimensional sinusoidal surface.
Data Types: single
| double
Complex Number Support: Yes
Output Arguments
c
— 2-D cross-correlation or autocorrelation matrix
matrix
2-D cross-correlation or autocorrelation matrix, returned as a matrix.
More About
2-D Cross-Correlation
The 2-D cross-correlation of an M-by-N matrix, X, and a P-by-Q matrix, H, is a matrix, C, of size M+P–1 by N+Q–1. Its elements are given by
where the bar over H denotes complex conjugation.
The output matrix, C(k,l), has negative and positive row and column indices.
A negative row index corresponds to an upward shift of the rows of H.
A negative column index corresponds to a leftward shift of the columns of H.
A positive row index corresponds to a downward shift of the rows of H.
A positive column index corresponds to a rightward shift of the columns of H.
To cast the indices in MATLAB® form, add the size of H: the element
C(k,l) corresponds to
C(k+P,l+Q)
in the workspace.
For example, consider this 2-D cross-correlation:
X = ones(2,3);
H = [1 2; 3 4; 5 6]; % H is 3 by 2
C = xcorr2(X,H)
C = 6 11 11 5 10 18 18 8 6 10 10 4 2 3 3 1
The C(1,1)
element in the output corresponds to
C(1–3,1–2) = C(–2,–1) in the
defining equation, which uses zero-based indexing. To compute the
C(1,1)
element, shift H
two rows up and
one column to the left. Accordingly, the only product in the cross-correlation sum
is X(1,1)*H(3,2) = 6
. Using the defining equation, you obtain
with all other terms in the double sum equal to zero.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced before R2006a
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