How to smoothen a plot?

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DARSHAN KUMAR BISWAS
评论： Image Analyst 2022-6-19
a=imread('IMG_0.jpg');
b=imread('IMG_20.jpg');
c=a(:,:,3)-b(:,:,3);
max(max(c));
min(min(c));
no_row=size(c,1);
no_col=size(c,2);
k=zeros(1,4857);
y=linspace(0,200,200);
l=zeros(45,200);
for m=1:45
for n=1:200
l(m,n)=mean(mean(c(((m-1)*10+1):10*m,((n-1)*24+1):24*n)));
end
end
o=zeros(1,200);
for i=1:200
p=l(:,i);
for j=1:45
if p(j)>=100
o(i)=j;
plot(y,(45-o)*20/45)
break
end
end
end
how do I smoothen the plot and get rid of the vertical lines?
  2 个评论
DARSHAN KUMAR BISWAS
DARSHAN KUMAR BISWAS 2022-6-19
Please keep in ming that I am a begineer and before matlab I have done work only in C loanguage.
Image Analyst
Image Analyst 2022-6-19
Yes, most people here are beginners. But that doesn't mean you cannot understand a direct request to attach your data? Anyone can understand that even if they don't know MATLAB programming. Just use the paperclip icon to attach a text file with the x and y data in it. Not sure what those two images you read in are, but go ahead and attach those two images just in case it helps explain something.

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Image Analyst
Image Analyst 2022-6-19
Try this (untested because you forgot to attach your data). Also it depends if you want to delete the bad points or it you want to change them to interpolated/repaired values.
% To remove bad elements:
badIndexes = y > y(end);
x(badIndexes) = [];
y(badIndexes) = [];
% Alternatively to leave them there but replace them with a quadratic fit:
goodIndexes = (y <= y(end)); % A logical vector.
% Now fit a quadratic to just the good values.
coefficients = polyfit(x(goodIndexes), y(goodIndexes), 2);
% Now get a fitted y.
yFixed = polyval(coefficients, x);
% Replace bad y values with fixed/repaired/interpolated ones.
y(~goodIndexes) = yFixed(~goodIndexes);
If you have any more questions, then attach your data and code to read it in with the paperclip icon after you read this:
TUTORIAL: How to ask a question (on Answers) and get a fast answer

更多回答（1 个）

John D'Errico
John D'Errico 2022-6-19
编辑：John D'Errico 2022-6-19
Simple. Delete the points in the beginning of the curve that give you the spikes. WTP? They will be easy enough to spot, as they will have an unreasonably large y value compared to the rest.
If the result is STILL too noisy, then use smooth on it. Or appy a smoothing spline, if you hve the curve fitting toolbox. Or, to be honest, the remaining curve looks almost well enough behaved that you could fit it with a low order polynomial. You can do that using polyfit, or with the stats toolbox or the curve fitting toolbox. You might prefer the results from a robustfit, which will be than from a simple least squares fit, given the somewhat spiky data.
If you want a better answer, then you would need to attach a copy of your data to a comment, or by editting your original question.

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