Does the Fourier Transform Reveal the Magic of Video Segmentation?

In summary, the conversation is about the use of the Fourier transform in video segmentation for image processing. The speaker has applied 3-D fft on video frames and obtained magnitude and phase spectrum, which they have used to reconstruct the video frames. However, they have noticed that the intensity values of moving parts become dominant compared to stationary parts in the reconstructed frames. The speaker is curious about how this happened and suggests that it may be due to the phase modulation of moving pixels and the lack of modulation in stationary pixels. They are also asking if anyone has any insights on this matter.
  • #1
ramdas
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Magic of Fourier Transform?

Hello everyone,i am doing my project in image processing...
i have done video sementation using the Fourier transform . I applied 3-D fft on video frames ((gray image(2D)+no of video frames(1D)=3D) and Obtained magnitude and phase spectrum and reconstructed video frames back from the phase spectrum only.
i am doing coding part using Matlab software

I have found that moving part pixel intensity values becomes dominant (means its intensity values are increased so much) compared to stationary part intensities in reconstructed frames of original frames .(e.g.in waterfall and traffic on road, water part and moving car's intensity values are increased respectively compared to the stationary background). i want to know how did this happen?

can i say here that moving part is only detected and no stationary part,due to the " intraframe 'phase ' modulation of moving part pixels" and "no modulation of stationary part pixels as it has no 'movement'"...and it was phase modulation ??
 
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  • #2
i am adding my result

i am adding my result image in which i could detect only moving part i.e.vehicles in the video using reconstruction from phase only.but i want to know how does it happened
 

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  • #3
hello everyone,am i asking wrong question or something .??please do reply something
 

1. What is the Fourier Transform and why is it important in science?

The Fourier Transform is a mathematical tool that breaks down a signal into its individual frequency components. It is important in science because many natural phenomena can be described as a combination of different frequencies, and the Fourier Transform allows us to analyze and understand these phenomena in a more precise and meaningful way.

2. How does the Fourier Transform work?

The Fourier Transform converts a signal from the time domain to the frequency domain by representing the signal as a combination of sinusoidal waves. It does this by decomposing the signal into its individual frequency components through integration.

3. What are some applications of the Fourier Transform?

The Fourier Transform has many applications in science, engineering, and mathematics. It is commonly used in signal processing, image and sound analysis, data compression, and solving differential equations. It also has applications in fields such as quantum mechanics, optics, and statistics.

4. What is the difference between the Fourier Transform and the Inverse Fourier Transform?

The Fourier Transform and the Inverse Fourier Transform are mathematical operations that are inverse of each other. The Fourier Transform converts a signal from the time domain to the frequency domain, while the Inverse Fourier Transform converts it back from the frequency domain to the time domain.

5. Are there any limitations to the Fourier Transform?

Although the Fourier Transform is a powerful tool, it does have some limitations. One limitation is that it assumes the signal is periodic, meaning it repeats itself over time. It also assumes that the signal is continuous, which may not always be the case in real-world applications. Additionally, the Fourier Transform may not be suitable for analyzing signals with sudden changes or discontinuities.

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