Ok, that makes sense. Thanks Chuck.
On Mon, May 11, 2009 at 2:41 PM, Charles R Harris <charlesr.har...@gmail.com > wrote: > > > On Mon, May 11, 2009 at 9:40 AM, Chris Colbert <sccolb...@gmail.com>wrote: > >> at least I think this is strange behavior. >> >> When convolving an image with a large kernel, its know that its faster to >> perform the operation as multiplication in the frequency domain. The below >> code example shows that the results of my 2d filtering are shifted from the >> expected value a distance 1/2 the width of the filter in both the x and y >> directions. Can anyone explain why this occurs? I have been able to find the >> answer in any of my image processing books. >> >> The code sample below is an artificial image of size (100, 100) full of >> zeros, the center of the image is populated by a (10, 10) square of 1's. The >> filter kernel is also a (10,10) square of 1's. The expected result of the >> convolution would therefore be a peak at location (50,50) in the image. >> Instead, I get (54, 54). The same shifting occurs regardless of the image >> and filter (assuming the filter is symetric, so flipping isnt necessary). >> > > Your kernel is offset and the result is expected. The kernel needs to be > centered on the origin, aliasing will then put parts of it in all four > corners of the array *before* you transform it. If you want to keep it > simple you can phase shift the transform instead. > > Chuck > > > > _______________________________________________ > Numpy-discussion mailing list > Numpy-discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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