You've given me a lot to think about, and I appreciate your thoughtful response. My experience is definitely more high-level, where I typically don't have to think much about things like memory access and how data is moved.
Your questions about previous algorithms are valid, I didn't really give any details. I've implemented solutions before in matlab and lab view, but the questions you bring up are things I haven't really been forced to think about in those contexts. I have used the Fast Radial Symmetry Transform (FRST) previously, which I believe uses the Hough transform as you suggest. This one in particular I've read is well suited to parallel computing. That can determine the positions of the particles and be used to establish regions of interest, which I then need to extract further information about the focus of the particles. The simplest focus measure I've used is simply the image variance of each region which reaches a maximum when the particle is in sharp focus and drops off as it becomes blurry. Other measures I've used take advantage of the Newton ring pattern of the particles and are more computationally intense. In this case it involves calculating a radial intensity profile for each particle and fitting the curve with a model or pattern matching against a set of curves from a calibration.
I've got to go back and do my homework on this, thanks so much for your input.
KenStatistics: Posted by halvorka — Tue Sep 17, 2013 12:15 pm
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