I found the function cpcorr() very useful to fine-tune control points I had selected elsewhere before using Matt J's solution. Another solution I found was to takemy control points I found using corners(), refine them with cpcorr(), and finally plug them into fitgeotrans(). Hopefully this helps someone as much as it helped me!
Image Alignment Code in MATLAB
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Joe on 11 Oct 2013
Commented: Sherwin Theophilus on 16 May 2022
I've been trying everything I can find, but I can't seem to come up with a code that aligns my images very well. I have been using the pressure taps on the image to align the images manually, so using those again would be great. I've gotten to within a few pixels, but I need it to be with at least pixel accuracy, ideally subpixel accuracy. Here's an example of one of the codes that seems to do semi well.
img1 = 255-mean(imread('01_wing.png'),3);
img2 = 255-mean(imread('02_wing.png'),3);
c = normxcorr2(img2,img1);
[y x] = find(c==max(c(:)));
y = y-size(img2,1);
x = x-size(img2,2);
TFORM = maketform('affine',[1 0 x;0 1 y; 0 0 1]');
NEWimg2 = imtransform(img2,TFORM,'xdata',[1 size(img1,2)],'ydata',[1 size(img1,1)]);
I've attached the wing images in a zip file. The image below is an example of the pressure taps not aligning correctly.
What should I do differently?
Edited: Matt J on 15 Oct 2013
If you can extract the coordinates of the pressure taps in each image e.g., using regionprops(...,'WeightedCentroid'), then you could try using
to find the transformation that aligns them.
Even better would be if you already knew the 2D coordinates of the pressure taps with high accuracy in some prior template image. Then you could register both '01_wing.png' and '02_wing.png' to that.
More Answers (2)
Image Analyst on 11 Oct 2013
Why not use imregister()? It's in the Image Processing Toolbox.
Edited: Matt J on 15 Oct 2013
I can imagine imregister() having difficulty if the pressure taps in image A do not have any initial overlap with the taps in image B. The initial gradient of the registration metric will be zero in that case and the algorithm won't be able to move.
With normxcorr2, you would hopefully be able to obtain that initial overlap, but since the taps are so small, it is unclear.
Here's what works well for me:
1. Get dftregistration.m from www.mathworks.com/matlabcentral/fileexchange/18401-efficient-subpixel-image-registration-by-cross-correlation
2. Register the gradients of the images, not the images themselves.
Here's my code:
im1Orig = double(imread('01_wing.png'));
im2Orig = double(imread('02_wing.png'));
im1 = imgradient(im1Orig);
im2 = imgradient(im2Orig);
%Get registration values
[output, Greg] = dftregistration(fft2(im1),fft2(im2),50);
imOut = TranslateImage(im2, output(3), output(4),'method','Fourier');
I get a row shift of -0.36 pixels and a column shift of +3.06 pixels. My function TranslateImage is basically:
img_out = TranslateImage(img_in, rowshift, colshift)
%%Create frequency space sampling vectors
[numrows, numcols] = size(img_in);
[n,m] = meshgrid(-fix(numcols/2):fix((numcols-1)/2),-fix(numrows/2):fix((numrows-1)/2));
m = m/numrows;
n = n/numcols;
%%Perform translation in the Fourier domain
img_fft = fftshift(fft2(ifftshift(double(img_in))));%Cast the input image to a double
shiftOtf = exp(-1i*2*pi*m*rowshift) .* exp(-1i*2*pi*n*colshift);
img_fft_trans = img_fft .* shiftOtf;
img_out = real(fftshift(ifft2(ifftshift(img_fft_trans))));
For monomodal image registration with pure translation, there are many algorithms that work better than imregister().
I should point out that pure translation doesn't appear to be totally valid for these images, but it's close. Some of the features line up very well, while others are still off by a pixel.
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