Computer Vision MT25, Sampling and reconstruction
Flashcards
@Define sampling.
Recording the values of a function at a discrete set of locations.
@Define reconstruction.
Converting a sampled representation back into a continuous function by “guessing” what happens between the samples.
@Visualise why it’s important that you sample a signal with a sufficiently high frequency.
@Define aliasing.
The distortion of high-frequency content in a signal when it is sampled at an insufficient rate.
@State the Nyquist-Shannon sampling theorem.
When sampling a signal at discrete intervals, the sampling frequency must be at least twice the maximum frequency of the input signal to allow perfect reconstruction of the original signal.
@Describe two ways that you could prevent aliasing.
- Sample more often
- Remove all frequencies greater than half the sampling frequency, e.g. using smoothing or a low-pass filter