finding dense location and define centeriod of the each dense location

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SatyaPrakash Gupta 2020-4-15

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https://ww2.mathworks.cn/matlabcentral/answers/518015-finding-dense-location-and-define-centeriod-of-the-each-dense-location

评论： Image Analyst 2020-4-17

rcsFiltered_raw.mat

I have attached .mat file and below zoomed figure of one section , I would like to find the centeriod of the dense location.

Since I have huge data around 400000 * 2, i am not able to use DBSCAN.

can you please do help me out with the loaded data ?

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darova 2020-4-16

Can i ask why do you need so many centroids? What are you doing further with them

SatyaPrakash Gupta 2020-4-16

编辑：SatyaPrakash Gupta 2020-4-16

ImageFilteredData.mat

@darova :

Yes at the end i need to connect those line to each centriod where i am stuck now.

please find the attached data where i have found the centriods.

do you have any idea how can we connect them ?

how to further separate the data which below to left side and right side ?

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darova 2020-4-16

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Untitled1.m

Here is simple but coarse (maybe not precise method)

Fill matrix Connect pixels Thin line Remove 'spur'

Results (green point - rounded orinal data)

I suppose you want to order those points now (connect them)

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SatyaPrakash Gupta 2020-4-17

编辑：SatyaPrakash Gupta 2020-4-17

leftRightCoord.mat

Sorry this trick did not helped me, now i have a data separated with left and right , is it possible to connect a single line on left and single line on right side .

The line should break if the distance is more than 10m moving forward and start again with a new line for distance smaller than 10m

darova 2020-4-17

I don't understand the question. This is your data:

Can you make a simple sketch of the result you expect?

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Image Analyst 2020-4-17

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/518015-finding-dense-location-and-define-centeriod-of-the-each-dense-location#answer_426628

编辑：Image Analyst 2020-4-17

Looks like you already have an answer that you've accepted, however one way to approach it is to use fitPolynomialRANSAC() - it's in the Computer Vision Toolbox, if you have it. You can use this function twice. The first time it will get the first curve (one of them, not sure which one though). Then remove those points close to the first curve, and run it again to get the second curve and points close to it. fitPolynomialRANSAC() will return the polynomial coefficients of the curve and the indexes of the data points that are close to that curve.

Also see RANSAC in Wikipedia if you're not familiar with the RANSAC concept.