2
votes

I am writing a matlab code that takes in a photo and detects the circular object. For example, the function takes a picture of a peach (circular object) as an input and will return the same image with the peach circled.

Currently, I am using hough transform, utilizing imfindcircles function. However, this function requires me to specify radius range and some sort of sensitivity/threshold value. These values differ for different sizes of image and round objects. So, to get the desired output, I will have to manually change these values for each input image, which is not what I want. I'm going to use this function on 100+ images, so it's impossible for me to do this manually.

My question is is there any way I can make my circular object detection function less manual and possibly completely automatic (does not require me to input any values, just the image)?

3
Your question is not about programming. Consider posting in the signal processing site.Cape Code

3 Answers

1
votes

Complexity of circle detection

The Hough transform is a voting procedure that requires assumptions be made about the minimum and maximum radii of your circles. Generally speaking using the Randomized Hough Transform for Circles you would pick three-points and then try to form a circle and check if the radius is within the desired range. Running this for a good number of iterations you should find peaks (multiple hits) in your accumulator matrix that represent circles. If you didn't make any assumptions about object size I think it is obvious this method wouldn't work.

0
votes

Do some routine pre-processing to adjust for contrast and brightness e.g. contrast stretching, histogram equalization. If you might have some noise in the images, then apply bit of gaussian smoothing as well.

Normalizing images this way will reduce inter-image variance and help you with setting thresholds.

0
votes

the Hough Transform can be used to detect circles, lines, etc.You can refer the demos in Matlab. There are several cases for the application of Hough Transform.