Extract curve/surface from an 2-D area of points not equivalently spaced

I have data which represents the filled area of two curves. See image. I need to work with the superficial curve (more or less like a sinoidal signal), but I do not see a way to extract only the maximum and minimum points. It is important to know the points are not evenly or equivalently distributed and there isn't a calculated sampling frequency.

This is an example of the data I used to represent the image (my matrix is bigger).

Example = reshape([0.016536 0.036051 0.055566 0.075081 0.094596 0.114111 0.127121 0.134927 0.140131 0.153141 0.153141 0.160947 0.167451 0.172655 0.172655 0.179160 0.180461 0.185665 0.185665 0.188060 0.193797 0.198675 0.198675 0.198675 0.198675 0.198675 0.205180 0.211685 0.212986 0.212986 0.218190 0.218190 0.218190 0.219491 0.219491 0.231200 0.231200 0.232501 0.232501 0.237705 0.237705 0.237705 0.237705 0.237705 0.239331 0.247463 0.250715 0.250715 0.252341 0.252341 0.257220 0.257220 0.257220 0.257220 0.257220 0.257220 0.257220 0.270230 0.270230 0.270230 0.270230 0.270230 0.276735 0.276735 0.276735 0.276735 0.276735 0.276735 0.278361 0.286493 0.289745 0.289745 0.296250 0.296250 0.296250 0.296250 0.296250 0.296250 0.296250 0.306007 0.309260 0.309260 0.312512 0.315765 0.315765 0.315765 0.315765 0.315765 0.328775 0.330401 0.332027 0.332027 0.335280 0.335280 0.348290 0.348290 0.349591 0.366178 0.367805 0.387320 0.403582 0.408461 0.425049 0.427976 0.442612 0.445864 0.447491 0.465379 0.465379 0.465379 0.483268 0.484894 0.496603 0.504409 0.510914 0.510914 0.517419 0.517419 0.523924 0.523924 0.523924 0.523924 0.523924 0.530429 0.530429 0.530429 0.536934 0.536934 0.536934 0.543439 0.543439 0.543439 0.543439 0.543439 0.543439 0.549944 0.549944 0.549944 0.549944 0.549944 0.556449 0.556449 0.556449 0.558075 0.562954 0.562954 0.562954 0.562954 0.562954 0.562954 0.569459 0.569459 0.569459 0.569459 0.575964 0.575964 0.575964 0.575964 0.575964 0.582469 0.582469 0.582469 0.582469 0.582469 0.582469 0.588974 0.588974 0.588974 0.588974 0.588974 0.595479 0.595479 0.595479 0.595479 0.601984 0.601984 0.601984 0.601984 0.601984 0.601984 0.608489 0.608489 0.608489 0.608489 0.614994 0.614994 0.614994 0.616620 0.621499 0.621499 0.621499 0.621499 0.621499 0.628004 0.628004 0.628004 0.629630 0.634509 0.634509 0.641014 0.641014 0.641014 0.641014 0.641014 0.641014 0.647519 0.647519 0.654024 0.654024 0.654024 0.660529 0.660529 0.660529 0.660529 0.667034 0.667034 0.673539 0.673539 0.675165 0.680044 0.680044 0.686549 0.686549 0.688175 0.693053 0.699558 0.699558 0.699558 0.706063 0.712568 0.719073 0.719073 0.720700 0.732083 0.735336 0.740215 0.751598 0.753225 0.771113 0.790628 0.810143 0.829658 0.849173 0.868688 0.888203 -0.053191 -0.053191 -0.053191 -0.053191 -0.053191 -0.046647 -0.033559 0.044970 0.195483 0.290372 0.035154 0.457245 0.182395 0.624118 0.018794 0.300188 0.457245 0.705918 0.133315 0.810811 0.015522 0.957864 0.604486 0.470333 0.352540 0.234747 0.781175 0.005706 1.085473 0.143131 0.483421 0.365628 0.260923 0.873446 0.637860 1.020033 -0.007382 1.179707 0.119572 0.745183 0.627390 0.509597 0.391804 0.274012 0.879336 0.005706 1.137825 1.020033 1.271978 0.094050 0.902240 0.784447 0.666654 0.548861 0.431069 0.313276 0.221659 1.281794 1.164002 1.046209 0.103866 0.005706 0.797535 0.679742 0.561949 0.444157 0.326364 0.208571 0.938232 1.203266 1.085473 0.054786 0.918600 0.784447 0.666654 0.548861 0.431069 0.313276 0.221659 1.006944 0.094050 0.005706 0.830255 0.719007 0.601214 0.483421 0.365628 0.247835 0.012250 0.133315 0.575038 0.457245 0.352540 0.260923 0.005706 0.300188 0.143131 0.015522 0.130043 0.025338 0.005706 0.094050 0.021411 0.172579 0.005706 0.077690 0.211843 0.234747 0.116954 0.012250 0.015522 0.143131 -0.155278 -0.328041 -0.485098 -0.596346 -0.131719 -0.720683 -0.249512 -0.367305 -0.838476 -0.956269 -1.067518 -0.485098 -0.602890 -1.191854 -0.131719 -0.720683 -1.309647 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.545233 -1.656481 -0.131719 -0.720683 -1.309647 -1.741554 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.545233 -0.131719 -0.720683 -1.309647 -1.663026 -1.780818 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.545233 -1.761186 -0.131719 -0.720683 -1.309647 -1.672842 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.532145 -0.131719 -0.720683 -1.427440 -1.329279 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -0.485098 -0.602890 -1.309647 -1.211486 -0.131719 -0.720683 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.191854 -0.485098 -0.602890 -0.131719 -0.720683 -1.054429 -0.249512 -0.367305 -0.838476 -0.949725 -0.485098 -0.602890 -0.131719 -0.818844 -0.740315 -0.249512 -0.367305 -0.485098 -0.720683 -0.622523 -0.131719 -0.249512 -0.367305 -0.583258 -0.504730 -0.131719 -0.249512 -0.485098 -0.386937 -0.131719 -0.357489 -0.269144 -0.249512 -0.151351 -0.125175 -0.085911 -0.059735 -0.053191 -0.053191 -0.033559 -0.033559 ],[245 2]);

I will appreciate any hint to a function I can use or a method. I tried with some functions like mesh or findpeaks but didn't work.

[KSSV]

Allready if you have two curves then why to fill area and get the boundary again?

[Roberto Tornero Costa]

Maybe I didn't explain properly. The plot was extracted from an image by an automated tool. This an example of the matrix I got for X and Y. I don't have the values for the curves. That's what I want to obtain.

Answer 1

[Star Strider]

I am not certain what result you want.

The Signal Processing Toolbox envelope funciton is one option, and since I am familiar with it, I am using it here.

If you do not have that Toolbox, experiment with the boundary or the polyshape boundary functions.

Try this (using envelope) —

Example = reshape([0.016536 0.036051 0.055566 0.075081 0.094596 0.114111 0.127121 0.134927 0.140131 0.153141 0.153141 0.160947 0.167451 0.172655 0.172655 0.179160 0.180461 0.185665 0.185665 0.188060 0.193797 0.198675 0.198675 0.198675 0.198675 0.198675 0.205180 0.211685 0.212986 0.212986 0.218190 0.218190 0.218190 0.219491 0.219491 0.231200 0.231200 0.232501 0.232501 0.237705 0.237705 0.237705 0.237705 0.237705 0.239331 0.247463 0.250715 0.250715 0.252341 0.252341 0.257220 0.257220 0.257220 0.257220 0.257220 0.257220 0.257220 0.270230 0.270230 0.270230 0.270230 0.270230 0.276735 0.276735 0.276735 0.276735 0.276735 0.276735 0.278361 0.286493 0.289745 0.289745 0.296250 0.296250 0.296250 0.296250 0.296250 0.296250 0.296250 0.306007 0.309260 0.309260 0.312512 0.315765 0.315765 0.315765 0.315765 0.315765 0.328775 0.330401 0.332027 0.332027 0.335280 0.335280 0.348290 0.348290 0.349591 0.366178 0.367805 0.387320 0.403582 0.408461 0.425049 0.427976 0.442612 0.445864 0.447491 0.465379 0.465379 0.465379 0.483268 0.484894 0.496603 0.504409 0.510914 0.510914 0.517419 0.517419 0.523924 0.523924 0.523924 0.523924 0.523924 0.530429 0.530429 0.530429 0.536934 0.536934 0.536934 0.543439 0.543439 0.543439 0.543439 0.543439 0.543439 0.549944 0.549944 0.549944 0.549944 0.549944 0.556449 0.556449 0.556449 0.558075 0.562954 0.562954 0.562954 0.562954 0.562954 0.562954 0.569459 0.569459 0.569459 0.569459 0.575964 0.575964 0.575964 0.575964 0.575964 0.582469 0.582469 0.582469 0.582469 0.582469 0.582469 0.588974 0.588974 0.588974 0.588974 0.588974 0.595479 0.595479 0.595479 0.595479 0.601984 0.601984 0.601984 0.601984 0.601984 0.601984 0.608489 0.608489 0.608489 0.608489 0.614994 0.614994 0.614994 0.616620 0.621499 0.621499 0.621499 0.621499 0.621499 0.628004 0.628004 0.628004 0.629630 0.634509 0.634509 0.641014 0.641014 0.641014 0.641014 0.641014 0.641014 0.647519 0.647519 0.654024 0.654024 0.654024 0.660529 0.660529 0.660529 0.660529 0.667034 0.667034 0.673539 0.673539 0.675165 0.680044 0.680044 0.686549 0.686549 0.688175 0.693053 0.699558 0.699558 0.699558 0.706063 0.712568 0.719073 0.719073 0.720700 0.732083 0.735336 0.740215 0.751598 0.753225 0.771113 0.790628 0.810143 0.829658 0.849173 0.868688 0.888203 -0.053191 -0.053191 -0.053191 -0.053191 -0.053191 -0.046647 -0.033559 0.044970 0.195483 0.290372 0.035154 0.457245 0.182395 0.624118 0.018794 0.300188 0.457245 0.705918 0.133315 0.810811 0.015522 0.957864 0.604486 0.470333 0.352540 0.234747 0.781175 0.005706 1.085473 0.143131 0.483421 0.365628 0.260923 0.873446 0.637860 1.020033 -0.007382 1.179707 0.119572 0.745183 0.627390 0.509597 0.391804 0.274012 0.879336 0.005706 1.137825 1.020033 1.271978 0.094050 0.902240 0.784447 0.666654 0.548861 0.431069 0.313276 0.221659 1.281794 1.164002 1.046209 0.103866 0.005706 0.797535 0.679742 0.561949 0.444157 0.326364 0.208571 0.938232 1.203266 1.085473 0.054786 0.918600 0.784447 0.666654 0.548861 0.431069 0.313276 0.221659 1.006944 0.094050 0.005706 0.830255 0.719007 0.601214 0.483421 0.365628 0.247835 0.012250 0.133315 0.575038 0.457245 0.352540 0.260923 0.005706 0.300188 0.143131 0.015522 0.130043 0.025338 0.005706 0.094050 0.021411 0.172579 0.005706 0.077690 0.211843 0.234747 0.116954 0.012250 0.015522 0.143131 -0.155278 -0.328041 -0.485098 -0.596346 -0.131719 -0.720683 -0.249512 -0.367305 -0.838476 -0.956269 -1.067518 -0.485098 -0.602890 -1.191854 -0.131719 -0.720683 -1.309647 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.545233 -1.656481 -0.131719 -0.720683 -1.309647 -1.741554 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.545233 -0.131719 -0.720683 -1.309647 -1.663026 -1.780818 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.545233 -1.761186 -0.131719 -0.720683 -1.309647 -1.672842 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.427440 -0.485098 -0.602890 -1.191854 -1.532145 -0.131719 -0.720683 -1.427440 -1.329279 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -0.485098 -0.602890 -1.309647 -1.211486 -0.131719 -0.720683 -0.249512 -0.367305 -0.838476 -0.956269 -1.074062 -1.191854 -0.485098 -0.602890 -0.131719 -0.720683 -1.054429 -0.249512 -0.367305 -0.838476 -0.949725 -0.485098 -0.602890 -0.131719 -0.818844 -0.740315 -0.249512 -0.367305 -0.485098 -0.720683 -0.622523 -0.131719 -0.249512 -0.367305 -0.583258 -0.504730 -0.131719 -0.249512 -0.485098 -0.386937 -0.131719 -0.357489 -0.269144 -0.249512 -0.151351 -0.125175 -0.085911 -0.059735 -0.053191 -0.053191 -0.033559 -0.033559 ],[245    2])

Example = 245×2

0.0165 -0.0532 0.0361 -0.0532 0.0556 -0.0532 0.0751 -0.0532 0.0946 -0.0532 0.1141 -0.0466 0.1271 -0.0336 0.1349 0.0450 0.1401 0.1955 0.1531 0.2904

[envh,envl] = envelope(Example(:,2), 5, 'peak');

Lvh = envh >= 0;
Lvl = envl <= -0.01;

figure
plot(Example(:,1), Example(:,2), 'DisplayName','Data')
hold on
plot(Example(Lvh,1), envh(Lvh), '-r', 'LineWidth', 1.5, 'DisplayName','Upper Envelope')
plot(Example(Lvl,1), envl(Lvl), '-g', 'LineWidth', 1.5, 'DisplayName','Lower Envelope')
hold off
grid
legend('Location','best')
title('Original Showing Upper & Lower Envelopes')

envv = zeros(size(Example(:,1)));
envv(Lvh) = envh(Lvh); 
envv(Lvl) = envl(Lvl);

% envvbuf = buffer(envv,10)

figure
plot(Example(:,1), Example(:,2), 'DisplayName','Data')
hold on
plot(Example(:,1), envv, '-r', 'LineWidth',1.5, 'DisplayName','Concatenated Envelopes')
hold off
grid
legend('Location','best')
title('Original Showing Concatenated Envelopes')

.

Answer 2

[Matt J]

I think it would be better to go back to the original image and use edge.