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<body>Hi Luis,<BR>
<BR>
Thanks for the reply, I thought the itk community was dead in the new year.<BR><BR>
I need to detect the position and diameter of randomly distributed particle-images where it is assumed each particle-image has a Gaussian profile.<BR> <BR>> 1) How fast do you need the circle detection to be ?<BR><BR>
The circle detection algorithm must be fast, it is applied to an ensemble of particle-images with Gaussian profile. Each image may contain tens to hundreds of particle-images.<BR>
<BR>> 2) How big are your images ?<BR><BR>
About 1k X 1k. Each particle-image could have a diameter from 3 to 10 pixels.<BR>
<BR>> 3) Are you looking for full circles ?<BR>> or only for circumferences ?<BR><BR>
The circle (particle-image) centre and diameter (Gaussian profile diameter) are desirable to be detected.<BR>
<BR>> <BR>> 4) Do you expect to find multiple circles per image ?<BR><BR>
As described, yes.<BR>
<BR>> <BR>> 5) Do you have any priori information about the circle ?<BR>> (eg. expected positions, expected radius, expected<BR>> intensities)<BR><BR>
Positions are random, diameters are variable (3 to 10 pixels) the intensities are variable too. The only prior knowledge could be the assumed Gaussian profile of each particle-image. Correlation-based methods may work, but I am sure there are faster methods around. The use of the existing code in the library is preferred.<BR>
<BR>><BR>> 6) Have you profiled the time that it takes to run the<BR>> Hough circle detection in a release (optimized) build ?<BR>> <BR><BR>
No, but the following is a quote from 'Practical Algorithms for Image Processing', Seul et al, Cambridge University Press 2000, 4.10.2:<BR>
<BR>
'..., and it is for this reason that the Hough transform is far less popular for geometrical objects than the line'.<BR><br /><hr />Be one of the first to try <a href='http://ideas.live.com/programpage.aspx?versionId=5d21c51a-b161-4314-9b0e-4911fb2b2e6d' target='_new'>Windows Live Mail.</a></body>
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