
Matrix Convolution with SubPixel Resolution 1.0 File ID: 84536 


 Matrix Convolution with SubPixel Resolution 1.0 License: Shareware File Size: 10.0 KB Downloads: 35
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Matrix Convolution with SubPixel Resolution 1.0 Description 

Description: Tristan Ursell Subpixel Resolved 2D Convolution March 2012 matout=matoverlay(mat1,mat2,x,y); This function takes an input matrix mat1 and creates an image of the matrix mat2 at the position (x,y) in mat1. If (x,y) are floats, then the image is a subpixel bilinear representatoin of mat2 at position (x,y) in mat1. The output matrix will have the same size at mat1, with no edge effects. Essentially this is performing a sparse, fully valid convolution of mat2 and mat1 at the point (x,y) with the output size of mat1. The point (x,y) uses the imaging convention for the coordinate axes. The values of (x,y) can be floats, as long as they lie within the bounds of mat1. Combining this function with a forloop and weights creates a fully valid 2D subpixel resolution convolution  see Example  in contrast to conv2 which is limited to pixel resolution. see also: conv2 Example: N=50; x=1+99*rand(1,N); y=1+99*rand(1,N); mat1=zeros(100,100); mat2=mat2gray(fspecial('gaussian',[11,11],3)); I0=zeros(size(mat1)); ints=rand(1,N); for i=1:N I0=I0+ints(i)*matoverlay(mat1,mat2,x(i),y(i)); end figure; colormap(hot) subplot(1,2,1) imagesc(mat2) axis equal axis tight title('mat2') subplot(1,2,2) hold on imagesc(I0) plot(x,y,'bo') axis equal axis tight title('Sparse Convolution of mat1 and mat2')
License: Shareware Related: Limited, pixel, n3d50, rand, conv, Contrast, combining, Bounds O/S:BSD, Linux, Solaris, Mac OS X File Size: 10.0 KB Downloads: 35


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