This chapter has introduced you to the relationship between computer vision and digital signal processing. We have considered a video feed as a collection of many signals—one for each channel value of each pixel—and we have understood that repetitive motions create wave patterns in some of these signals. We have used the Fast Fourier Transform and its inverse to create an alternative video stream that only sees certain frequencies of motion. Finally, we have superimposed this filtered video atop the original to amplify the selected frequencies of motion. There, we summarized Eulerian video magnification in 100 words!
Our implementation adapts Eulerian video magnification to real time by running the FFT repeatedly on a sliding window of recently captured frames rather than running it once on an entire prerecorded video. We have considered optimizations such as limiting our signal processing to grayscale, recycling large data structures rather than recreating them, and using several...
