# Detect lines in scatter plot

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Douglas Anderson on 15 Jan 2019
Commented: Douglas Anderson on 27 Jul 2020
Hello,
I have, as an example, the following data plot:
The Y data are in integer increments, so the "lines" of data horizontally aren't really lines. The X data are also integral, but sampled much more finely. The X value of the vertical lines is what is relevant. They are not perfect lines though!
For example, the vertical line on the left (X = ~80) has a jog between Y = 10 and Y = 9; there are gaps on the third line (X = ~ 158) at Y = 16, and from Y=11 to Y=13; and there are doublets on several lines, like the far right line (X = ~613) at Y=16 and Y=17.
I have thought of Hough transform, but that seems like overkill, and I don't have Image Processing Toolbox. This really isn't a pattern recognition problem, is it? I do have Signal Processing Toolbox, but not sure if anything applies there.
I have 2018b, but am using this on 2017a. Am I missing something?
Thoughts are welcome! Thanks!
Doug Anderson
PS: The scatter in the X values for a given line varies from about 2 to 4, with separation of about 30 or 40 or so between lines. This is, of course, excluding the jog mentioned, and the doublets.
Douglas Anderson on 27 Jul 2020
Hello!
I am attaching a figure with the red marker as you requested. All of the lines are essentially subparallel to the y-axis. Sometimes there are a lot of blue circles at the bottom, related to the sensitivity. But as you suggested, I only want the points that are more in-line with the majority of the points.
Note that even if it appears that the lines are strictly vertical, you can see from the horizontal scale that in fact there are slight variations in what seems to be vertical. Both scales are integers, but a hell of a lot more in the horizontal than the vertical.
I do not currently have either the Statistics and Machine Learning Toolbox for kmeans() or the Computer Vision Toolbox for ransac(). Would one be preferable over the other?
Thank you to both of you for your suggestions!
Doug

R2017a

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