do you really need the edges or what is the actual purpose? Maybe there are better methods other than canny to achieve it...
Am 13.12.2013 um 21:25 schrieb Adam Hughes firstname.lastname@example.org:
Thanks for the quick reply.
I think that I can get better results if I tweak the parameters. The threshold parameter intuitively makes sense, but I'll have to read a bit to get familiar with sigma and the algorithm in general. Thanks for the explanation; it really helped. I will try out the erosion as well.
PS, do you have any feelings towards the applicability of circular hough to my image?
On Friday, December 13, 2013 3:03:30 PM UTC-5, Dan Farmer wrote: Hi Adam,
This can be the worst part of image processing, but I'm curious how much you played with the parameters to Canny? You probably know this, but canny already tries to close gaps (hysteresis thresholding). What you want to do is try to lower the low_threshold parameter (values above the high threshold value get initially labeled as edges, then it looks for pixels that are connected to edge pixels and whose value is
low_threshold to link the edges).
An easy/basic way to get rid of small fragments would be to start with morphological erosion.
On Fri, Dec 13, 2013 at 11:47 AM, Adam Hughes hughes...@gmail.com wrote:
I have several images of circular particles (see attached for an example) and I've been experimenting with automatic routines to find edges.
I've found that with Canny, I can get really nice edges, but the edges are not always connected. Thus, when I do fill-binary, many of my particles are not painted in due to slight breaks in the border returned by canny. Is there an ideal way to fix this, either by connecting "almost" connected canny edges? Additionally, what is the best way to filter out small fragments and/or non-circular edges?
I've attached an image of the canny outlines; you can see that I obviously want to get rid some of the regions that aren't associated with any particles. PS, the coloring of the outlines are based on the brightness of the image at that point underneath it, which has been hidden. (Would be happy to share the function if anyone wants it).
Lastly, I tried adapting the circular hough transform example:
But struggled with setting it up, due to a naive understanding of the algorithm. Given that my image has thousands of particles, but I know roughly the size distribution, would the circular hough transform be useful to me?
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