I appreciate the response. The image only moves up and down, and I’m only tracking one pixel. Then I have the vertical position y as a function of time t, y(t). Each frame the pixel changes location, always up and down. I track the position. I extract the frequency using the MATLAB example shown above. I don’t think I need to use another technique, I’m just wanting to confirm L and Fs are as I describe. Would you input Fs as frame rate and L and total frame number?.Scott said:Video data is three-dimensional: horizontal, vertical, and time.
None of the examples deal with images - let alone video data.
It is certainly possible to do a 3-d FFT, but I doubt that is your intent.
Tell us more about this video and exactly what you want to extract from it. Is it a pendulum swinging back and forth and you want the frequency?
This sounds like a situation where an FFT could be used - but it probably isn't the best method.
If you do use an FFT, you would probably want to take a slice of the image across those frames so that you ware dealing with width and time. The slice would be selected as containing the periodic motion. Then an FFT would highlight periodic motion (with frequency spikes) along the frequency (time) axis.
Yes.joshmccraney said:I appreciate the response. The image only moves up and down, and I’m only tracking one pixel. Then I have the vertical position y as a function of time t, y(t). Each frame the pixel changes location, always up and down. I track the position. I extract the frequency using the MATLAB example shown above. I don’t think I need to use another technique, I’m just wanting to confirm L and Fs are as I describe. Would you input Fs as frame rate and L and total frame number?
L would be better described as the "sample count".Fs = 1000; % Sampling frequency
T = 1/Fs; % Sampling period
L = 1500; % Length of signal
t = (0:L-1)*T; % Time vector
FFT stands for Fast Fourier Transform, which is a mathematical algorithm used to convert a signal from its original domain (such as time) to a representation in the frequency domain. The time vector refers to the time values of a signal, which can be interpreted using FFT to analyze its frequency components.
FFT works by breaking down a signal into its individual frequency components, which are represented as peaks in the frequency domain. The height of each peak indicates the strength of that frequency component in the original signal. By analyzing the frequency components, we can gain insights into the characteristics of the signal in the time domain.
FFT interpretation of the time vector is important because it allows us to analyze and understand the frequency components of a signal. This can be useful in various fields such as signal processing, acoustics, and vibration analysis. It can also help in identifying patterns and anomalies in time series data.
Some common applications of FFT interpretation of the time vector include audio and video compression, noise reduction, spectral analysis in physics and engineering, and biomedical signal processing. It is also widely used in digital signal processing and image processing.
While FFT is a powerful tool for analyzing signals in the frequency domain, it does have some limitations. It is most effective for signals with a fixed frequency content, and may not work well for signals with rapidly changing frequency components. Additionally, FFT can only provide information about the frequency components of a signal, and not its amplitude or phase information.