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read a periodic image(signal) and detect every wave then display every wave on separte figure help me please

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read a periodic iamge and detect every wave then display every wave on separte figure
help me please
  4 件のコメント
Image Analyst
Image Analyst 2013 年 12 月 16 日
Do you want to crop out each peak/hump and display it in its own plot? Or do you want to take the Fourier transform and display each harmonic?
ahmed
ahmed 2013 年 12 月 16 日
i want to crop out each peak/hump and display it in its own plot

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回答 (2 件)

Image Analyst
Image Analyst 2013 年 12 月 16 日
If you have the Signal Processing Toolbox, invert the curve and find the peaks. These are really the valleys. Then crop them out and use plot().
  12 件のコメント
ahmed
ahmed 2013 年 12 月 17 日
編集済み: ahmed 2013 年 12 月 17 日
can you help me I lost hope just i want a code or a function that do that ??
Image Analyst
Image Analyst 2013 年 12 月 17 日
The submission Walter is referring to is http://www.mathworks.com/matlabcentral/fileexchange/36904-matlab-script-for-digitizing-a-published-graph but it looks like ahmed is done with us because he's started all over again with a brand new discussion on the very same question.

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Walter Roberson
Walter Roberson 2013 年 12 月 20 日
If you really are reading from an image, use the mentioned contribution to analyze the image to get the data.
Once you have data, you can go the theoretic route by performing an autocorrelation. See http://www.mathworks.com/help/econ/autocorrelation-and-partial-autocorrelation.html and also xcorr().
Alternately you can use one of the peak-finding routines, possibly one from the MATLAB File Exchange. Look for the maximum absolute value of peak anywhere in the data, and then look for additional peaks that are "quite close" in magnitude (including being on the same side of 0)
As a first approximation the cycle length is equal to the distance between maximal peaks. Now break up the data into groups of that cycle length.
That first approximation algorithm will be wrong when a single cycle contains several maximal peaks, such as if the graph looked like
_^_^___^_^___^_^__
Working out how to deal with such cases is left as an exercise to the reader.

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