Finding Circle Edges :

Question

Finding Circle Edges :

Here are the two sample images that i have posted.

Need to find the edges of the circle:

Does it possible to develop one generic circle algorithm,that could find all possible circles in all scenarios ?? Like below 1. Circle may in different color ( White , Black , Gray , Red) 2. Background color may be different 3. Different in its size

http://postimage.org/image/tddhvs8c5/

http://postimage.org/image/8kdxqiiyb/

Please suggest some idea to write a algorithm that should work out on above circle

Answer 1

I think you will have to use a couple of different feature calculations that can be used for segmentation. I the first picture the circle is recognizeable by intensity alone so that one is easy. In the second picture it is mostly the texture that differentiates the circle edge, in that case a feature image based based on some kind of texture filter will be needed, calculating the local variance for instance will result in a scalar image that can segment out the circle. If there are other features that defines the circle in other scenarios (different colors for background foreground etc) you might need other explicit filters that give a scalar difference for those cases.

When you have scalar images where the circles stand out you can use the circular Hough transform to find the circle. Either run it for different circle sizes or modify it to detect a range of sizes.

If you know that there will be only one circle and you know the kind of noise that will be present (vertical/horizontal lines etc) an alternative approach is to design a more specific algorithm e.g. filter out the noise and find center of gravity etc.

Answer to comment: The idea is to separate the algorithm into independent stages. I do not know how the specific algorithm you have works but presumably it could take a binary or grayscale image where high values means pixel part of circle and low values pixel not part of circle, the present algorithm also needs to give some kind of confidence value on the circle it finds. This present algorithm would then represent some stage(s) at the end of the complete algorithm. You will then have to add the first stage which is to generate feature images for all kind of input you want to handle. For the two examples it should suffice with one intensity image (simply grayscale) and one image where each pixel represents the local variance. In the color case do a color transform an use the hue value perhaps? For every input feed all feature images to the later stage, use the confidence value to select the most likely candidate. If you have other unknowns that your algorithm need as input parameters (circle size etc) just iterate over the possible values and make sure your later stages returns confidence values.

Answer 2

Sounds like a job for the Hough circle transform:

I have not used it myself so far, but it is included in OpenCV. Among other parameters, you can give it a minimum and maximum radius.

Here are links to documentation and a tutorial.

I'd imagine your second example picture will be very hard to detect though

Answer 3

You could apply an edge detection transformation to both images.

Here is what I did in Paint.NET using the outline effect:

You could test edge detect too but that requires more contrast in the images.

Another thing to take into consideration is what it exactly is that you want to detect; in the first image, do you want to detect the white ring or the disc inside. In the second image; do you want to detect the all the circles (there are many tiny ones) or just the big one(s). These requirement will influence what transformation to use and how to initialize these.

After transforming the images into versions that 'highlight' the circles you'll need an algorithm to find them.

Again, there are more options than just one. Here is a paper describing an algoritm

Searching the web for image processing circle recognition gives lots of results.