- Surely there is a tool you can find in image processing to search for lines. Use it. Find all lines in the image.
- For EACH dark line found, compute the parameters of that line. You can use various tools to find the line coefficients in the form a*x + b*y = c. Do that for EVERY line.
- Consider every pair of lines found. Compute the intersection of each pair of lines. Store that intersection point in an array.
- Use a clustering tool on the intersections as found.

# How to detect cluster of radial lines which are generated from same point ?

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### Answers (4)

John D'Errico
on 4 Dec 2022

All of this should be quite doable.

##### 2 Comments

John D'Errico
on 8 Dec 2022

I think some problems are larger than others, and will require more CPU than others. But in fact, I recall that code to find lines in an image is not too difficult. Given each line, it is now trivial to compute the parameters of that line. (Be careful about the vertical lines!) So very little work there.

Finally, my idea was t ocompute the intersection points of pairs of lines. Even doing that for 500 lines, where you need to find the intersection now of 500*499 pairs of lines is still trivial for a computer. So I fail to see the problem. Just write the code efficiently. No, don't use the symbolic toolbox to find those intersection points. Good code would still be blazingly fast here.

Image Analyst
on 5 Dec 2022

Not sure what you want to find out since "detect" is so vague and unspecific. Please be specific. Like do you want a length distribution, or the point where all the lines seem to emanate from? How many clusters per image are there? Always 2, one on the left half and one on the right half? Or can there be multiple clusters per image?

Have you tried thresholding and then calling regionprops?

If you have any more questions, then reply back with specific questions, description of what you want to measure, and 2 or 3 more images so we can see how the images vary. But do so after you read this:

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Image Analyst
on 18 Dec 2022

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