How to do convolution of an image with many combinations of a mask, fast?

Hi, I have an normal size image (about 1200x1900) for which I have cropped to a smaller image, but for simplicity lets say i have to analyse the whole image which is the worst case.

I have made an mask of ellipse, which in my case is an aperture, and I want to determine how much pixel intensity I have within the aperture. For every new mask (aperture) I vary the size in both x and y axis. So I get a line up of different ellipse mask sizes. In addition I want to rotate the mask 180 degrees to find all possible combinations it may be poistioned relative to my image. Thus, I get X number of masks that I want to do convolution with my original image. Is there a way to do this process as fast as I can?

At the moment I have two nested for-loops, for which I vary the ellipse size in the x and y axis, and for each combination I have another for-loop that rotate the mask, and then do convolution (conv2) of image and mask. I grab the result (mask size, intensity, and rotation angle) that gives me highest pixel intensity and move on to next mask size.

Preferably I want to do this without the for-loops because as you can image that this takes quite some time (for every large ellipse sizes (masks)). Up to an hour for really large apertures.

Any ideas?

Thanks!

Answer 1

[Image Analyst]

I think that's just hte way it is. You're not limited by the for loops, that's for sure. You can do literally millions of for loop iterations in a fraction of a second - just try it and see. Have a for loop with something very simple in it, like assigning a variable and you'll prove it to yourself. The real problem is the large convolutions. Not only are they large, or can be, but there are lots of them. I think you're stuck since the convolution functions conv2() and imfilter() are already highly optimized.

For what it's worth, I do a similar thing in the attached demo where I rotate a kernel to enhance fibers.