On Nov 30, 7:38=A0pm, Eizo <odpe...@[EMAIL PROTECTED]
> wrote:
> On Nov 30, 2:18=A0pm, Manuel Matias <manuel.mat...@[EMAIL PROTECTED]
> wrote:
>
>
>
> > On Nov 29, 9:41=A0pm, Eizo <odpe...@[EMAIL PROTECTED]
> wrote:
>
> > > On Nov 29, 10:19=A0pm, serg271 <serg...@[EMAIL PROTECTED]
> wrote:
>
> > > > On Nov 29, 7:00=A0pm, Eizo <odpe...@[EMAIL PROTECTED]
> wrote:> Hi,
> > > > > I am looking for some (fast) algorithm to find circles in image.
> > > > > I understand that there is the usual Matlab routine that use
Hugh
> > > > > transform, but it is slow, and I need some other fast solution.
> > > > > any ideas?
>
> > > > I would suggest just brute-force it. Make edge detection, or
> > > > thresholding/segmentation. After that for each contour (closed
edge=
or
> > > > segment boundary) check if it circle or not - for example
calculate
> > > > center (average) and average radial error. Works pretty well for
> > > > convex polygons.
>
> > > Well, I am using a picture, that isn't super clear (e.g could be
> > > places where the edge isn't closed )
> > > also I think that computing whatever a contour is closed or not is
> > > very time consuming ( correct me if I Am wrong here..)
> > > and I need something that work =A0very fast.
>
> > You don't really need to check if the contour is closed.
> > The strategy Eizo suggested will work well on a contour
> > that is _almost_ closed, lacking a small segment
> > (but not if there is a large segment missing or if the
> > circunference is broken in two or more places).
>
> > Also, you can improve the results in several ways:
> > - Preprocess the original image to remove noise
> > - Choose well the edge detector that is better suited
> > =A0 for your task (sounds like Canny's algorithm might
> > =A0 help).
> > - After segmentation, use morphological operations
> > =A0 (closing should be useful) to have more compact,
> > =A0 better connected components.
>
> > Also, posting an example image will help us with
> > the specifics of your problem.
>
> Thanks,
>
> the work concentrate mainly on road signs. as such it has to be fast,
> also the image is not very good quality, but you can imagine a gray
> scale image of a sign, and =A0a little bit of background.
Road signs have distinct colored edge.
You can apply some color filter, convert to grayscale and then segment
image according to intensity.
Find connected components and check if they are circles.
Also google for "road sign recognition detection" - it seems there are
plenty research going in that area.
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