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nonlinear 2d calibration of line-camera using known pattern - how to align world coordinates to points?

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arnold on 2 Jul 2016
Commented: praveen rai on 28 Dec 2017
I rephrased the question, see last comment
Hi all,
I'm currently working with a highres line camera scanner and we want to use it for measurements. Therefore we need to be able to calibrate it properly in 2d.
We have an accurate test-pattern made by photolithography. We can assume it to be 'exact'. I have managed to come up with a very robust subpixel circle recognition to find the points of interest so I have no question regarding image recognition.
What is unclear to me at this point is how to use the found image coordinates together with the known spatial correlation in order to come up with a distortion map which ultimately would be used to correct the distortion.
The example shows a cropped subset of a typical image of circles with 400µm xy spacing. The recognition is already done (red circles with blue centercross) leaving out structures that are too close to the edge. Now, how do I calculate a distortion-map from that?
I played around with some algorithms like knnsearch in order to look at the lokal distances and I can see that they vary significantly. I don't want to simply create a coordinate system using arbitrary points and then measure relative distances to that system because I fear that might induce large errors over big distances.
My guess is that using a combined approach of the local information (distances) in combination with the 'far field' (straight lines, orthogonality etc.) should result in the most robst outcome but I'm stuck on how to continue. In the end we want to do many measurements in order to determine the measurement errors caused by vibration and thermal influences i.e.
I'm fairly certain this has been solved (many) times but as a non-expert on the field I just don't know what to search for exactly. I would appreciating any help!
praveen rai
praveen rai on 28 Dec 2017
@arnold can u provide me the code that how u find robust subpixel circle recognition bcz i want to find the square

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Answers (1)

Image Analyst
Image Analyst on 2 Jul 2016
I'd look at the average inter-row spacing as a function of row. It should be uniform. If there's a bias, you can determine that and use it to correct your real, non-calibration images. If you scan the calibration target multiple times and find that the inter-row distance is not deterministic and basically random, then there's not much you can do.

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