I have an image processing application that is different than anything that I've done before, and am looking for suggestions on how to approach. Here is a basic description of the problem:

  1. Given a set of images taken of over time, an "intensity wave" results for each pixel in the image, i.e. if you plot the intensity value of each pixel vs time, a wave plot results.

  2. Find pixels that have "similar" waves (in terms of both shape and phase) and put them into a bucket. It is unknown beforehand how many buckets there will be. It would be great to be able to define a required level of "correlation" between waves before they are put into the same bucket.

  3. At the end of the analysis, I want to look at how many buckets there are, how many pixels are in each buckets, and the spatial map over the image of each bucket.

I've considered doing an FFT for each "pixel wave" and then comparing the results of that...not sure if that would work. Any ideas or suggestions would be greatly appreciated.


PS Also please let me know if you think that there is a better forum to submit this question.


1 Answer 1


I'll propose a short answer and develop it if you need more information So, step 1 and 3 are somehow easy to implement

I would map step 2 into:

2.1) From each wave extract a set of features. This step basically defines what it means for tow waves to be similar and what information from waves is irrelevant.

2.2) Cluster the waves in buckets. Choose a method that computes the number of clusters automatically (you still have to specify a value that roughly defines the similarity between the data)

For 2.1 try to use more than one algorithm that can describe your wave. There are a wide variety of algorithms and your FFT ideea is good (that is, for each wave, compute FFT and use first k coefficients as features). Note that some algorithms generate features that are phase invariant, other keep the phase information. The choice is yours, whenever the phase information is useful or not.

For 2.2 you can use out of the box clustering algorithms. I suggest to start with the simpler ones, than climb up to more sophisticated approaches. There are mainly two categories, one for which you state the desired number of clusters and the other one where you specify something equivalent of a threshold and the algorithm clusters the data in as many buckets as necessary. Start with a k-means clustering and stop maybe at a mean shift clustering. Hierarchical clustering is another idea but could be hard to work with it from the beginning.

From my perspective step 2.2 is the most difficult to get it right. The meta parameter that controls the clustering is tricky to be tuned. Ideally you should have a way to measure independently how good the clustering is (ex. make a hand made bucketing that will be a ground truth for your method or derive some domain specific measures for the images). Based on the ground truth you can "tune" the meta parameter to an optimal value. The tuning is done using cross-validation or a simple train/evaluate splitting.

p.s. The clustering algorithm basically computes some distances between the features. You can measure directly the distance between two 1D signals with some methods (correlation, RMSE, etc) but in my opinion it limits your options in defining what it means for two waves to be similar.


I want to clarify what phase variant/invariant means.

Here is a picture with two signals (s1 and s2), that are very similar and in phase. signals

I performed FFT and extracted magnitude and phase (anlge) from each Fourier coefficient. (Note, the magnitude is log compressed so the absolute values are comparable)

Magnitude for each signal (m1 and m2): Magnitude Phase for each signal (p1 and p2): Phase

If I compute the Euclidean distance between the magnitudes and phases I get the following values:

For phase shift of 0 Magnitude distance: 6.2272 phase distance: 19.9371

I change the phase of the s2 so it will be in $\pi/2$ wrt to s1 and the values changed to:

For phase shift of 0.5 Magnitude distance: 5.9229 phase distance: 28.0612

Just for reference, here are the out of phase s1, s2, m1, m2, p1 and p2:

Signals: signalsPi Magnitude of the Fourier MagPi Phase: PhasePi

Note that the magnitude of in phase and out of phase signals are roughly unchanged. You can say that the magnitude of FFT for a signal, is insensitive to alterations of the phase (this is what the fancy "invariant" word means). The phase of FFT is another story, it is very sensitive to changes in the phase of a signal.

I post the MATLAB code so you can play with the simulations.

Hope it helps!

function FourierFeatures()
phaseShift = 0.5; % 0 == in phase, 0.5 = pi/2 out of phase

s1 = generateSignal(100,0.0);
s2 = generateSignal(100,phaseShift);
[m1 p1] = performFourier(s1);
[m2 p2] = performFourier(s2);

close all;
plot(s1,'r'); hold on; plot(s2,'b');
plot(m1,'r'); hold on; plot(m2,'b');
plot(p1,'r'); hold on; plot(p2,'b');

mdist = norm(m2-m1);
pdist = norm(p2-p1);

disp(['For phase shift of ', num2str(phaseShift) , ' Magnitude distance: ',num2str(mdist), ' phase distance: ' , num2str(pdist)]);

%Compute FFT Magnitude and phase
function [magnitude, phase] = performFourier(signal)
    f = fft(signal);
    magnitude = abs(f);
    magnitude = log(1+magnitude);
    phase = angle(f);

%Generate sinusoidal signals with some noise
function signal = generateSignal(length, offset)
    x = [1:length];
    omega = [10, 15 ,20];
    amplitude  = [10 10 15];
    randomAmp = [1 3 3];
    y = 5 * rand(1,length);
    for i = 1: size(omega,2)
        y = y + amplitude(i) * sin(offset * omega(i) + x * omega(i))+ randomAmp(i) * rand(1,length);
    signal = y;
  • $\begingroup$ THANKS!! This gives me some confidence that I've at least got a reasonable approach to investigate. I do have a follow up (hoping not to impose too much)... For 2.1 above, what would you suggest for a "phase invariant" characterization of the wave? I'm not sure that a plain vanilla FFT is phase invariant, but could be wrong. For 2.2, I've never used any clustering algorithms, but a quick Google found several k-means algorithms that I think I could implement. Thanks again. Great advice!! $\endgroup$ Commented Feb 21, 2014 at 21:24
  • $\begingroup$ CORRECTION: I need an FFT, or similar algorithm, that is phase VARIANT. I want to see a phase shift as a different wave. $\endgroup$ Commented Feb 21, 2014 at 21:34
  • $\begingroup$ Updated the answer. Hope it helps! $\endgroup$
    – visoft
    Commented Feb 23, 2014 at 12:51
  • $\begingroup$ Wow. Thank you for the great explanation and the time you put into it. I certainly must now try to "pay it forward" if I get the opportunity. Thanks again! $\endgroup$ Commented Feb 24, 2014 at 14:57

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