Detecting Many Small Circles in Close Proximity with cv::HoughCircles()

Question

I am trying to detect a large number of small circles that are in relatively close proximity to one another (only about 20 pixels apart) using OpenCV. I have managed to create this mask using cv::inRange() and cv::Canny().

Original Image

Mask

However, when I use cv::HoughCircles() only some of the circles are being detected accurately. Currently, I am using cv::HoughCircles() with the following parameters:

cv::HoughCircles(mat, circles, CV_HOUGH_GRADIENT, 2, mat.rows / 256, 100, 8, 2, 8);

Is this method not effective enough to detect circles that are this small and close together, or do I simply need to modify the parameters of cv::HoughCircles()?

Also, it would be useful to get rid of the "noise" surrounding the array of circles in the middle of the mask because some "false circles" are being detected around the edges of the mask. Is there a simple way to do this?

Answer 1

Get rid of the noise :

If you can make sure to always have the same environment parameters (e.g. distance from the circle, luminosity...), then you could mask your image just after the Canny edge detection, with cvAnd; here is what the mask would look like :

Hough circles detection :

Now, about HoughCircle. First, this function performs its own Canny edge detection. You are doing one too just before the call to HoughCircle. It may have an impact on the shapes of your circles, because of the way Canny works (i.e. intensity gradient on binary image...).

Speaking about the shape of your circles, just below is a close-up of what your "circles" look like; I would have been very impressed if HoughCircle actually did detect all or even just some of those. It can't give anything good in Hough space. Just to make sure, set the last two parameters to 0 (min/max radius), and try to lower the minimum distance between centers. But honestly, I think you need to find another approach to your problem.

[EDIT]

A possible approach would be to perform connected component labeling (e.g. blob detection). As far as I know it is not possible to do this simply with OpenCV alone, you will need something like cvblob, which is a very good OpenCV-based blob library. In particular, you might be interested in cvCentroid(CvBlob *blob).

Cheers

Answer 2

Hum, do you really need to detect them as circles? (as opposed to model them as circles).

If this is some kind of calibration pattern, and you are only interested in estimating the image positions of the centers, It may be a lot more efficient to detect them as point-like features first, then process each detected one individually - e.g. fitting a circle to a blob of white pixels in the neighborhood of each detected feature.