Le 23/02/2012 16:57, Tony Yu a écrit :
On Thu, Feb 23, 2012 at 10:02 AM, Zachary Pincus <zachary.pincus@yale.edu <mailto:zachary.pincus@yale.edu>> wrote:
Hi Siggi,
> like Andy suggested, I need the bonding box for each shear. This should represent the diameter in > good approximation what leads to the grain size distribution.
In this case I'd also agree with Andy that a template-matching approach might be best, with a library of templates of different sizes. Then for each template-match peak, determine which size template gives the best match, and read out the size distribution from that?
Alternately, you could look at a Hough-circles sort of approach, and again could read off the size distribution straight from the Hough-transformed image.
Zach
I have a hough-circles implementation (which I've been meaning to contribute). Unfortunately, my implementation does a pretty poor job when there's a large range of circle size and when the circles are close/overlapping. As a result, the output I get for the example image is not very good. A better implementation may be able to deal with these issues, though.
-Tony
Have you tried opencv's? Also their fitEllipse might be of help...
On Thu, Feb 23, 2012 at 11:25 AM, Guillaume Gay < guillaume@mitotic-machine.org> wrote:
Le 23/02/2012 16:57, Tony Yu a écrit :
On Thu, Feb 23, 2012 at 10:02 AM, Zachary Pincus <zachary.pincus@yale.edu>wrote:
Hi Siggi,
like Andy suggested, I need the bonding box for each shear. This should represent the diameter in good approximation what leads to the grain size distribution.
In this case I'd also agree with Andy that a template-matching approach might be best, with a library of templates of different sizes. Then for each template-match peak, determine which size template gives the best match, and read out the size distribution from that?
Alternately, you could look at a Hough-circles sort of approach, and again could read off the size distribution straight from the Hough-transformed image.
Zach
I have a hough-circles implementation (which I've been meaning to contribute). Unfortunately, my implementation does a pretty poor job when there's a large range of circle size and when the circles are close/overlapping. As a result, the output I get for the example image is not very good. A better implementation may be able to deal with these issues, though.
-Tony
Have you tried opencv's? Also their fitEllipse might be of help...
I'll have to look into that. I've used the opencv implementation of hough circles, but it didn't give me very good results---that could easily have been a problem on my end, though. -Tony
participants (2)
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Guillaume Gay -
Tony Yu