FFT = Fast Fourier transform

Engineering Terminology for artists

Will be focussing on continuous digital data: 1D sensors and 2D signals (images)

Even buttons have noise. Media artists must deal with noise:

 

Signals:

amplitude, frequency, period

Timbre: the shape of the wave (ex: square, ragged, curved)

Phase: phase must have two waves in relation to each other. They can cancel subtract or add to each other.

Pulse Width Modulations: duty cycle is the amount of time something is on

Spatial Frequency: visual signals have it all too ( amplitude, frequency, period and orientation)

different spatial frequencies convey different things about about an images:

high = detail, low = blur

Digital Signals:2 numbers characterize the sampling resolution:

Bit Depth

Sampling Rate

Nyquist Rate & Aliasing: nyquist rate is 1/2 the sampling rate. Any frequency higher than  two times the sampling rate will be aliased ( distorted and represented as a lower frequency)

line fitting: least squares line fitting. opencv

Forier: ways of representing a complex sound as a combination of different waves. This allows you to re-create a sound. see visually in stereography

can also see the the fft of an image. (has orientation unlike stereography) can reconstruct an image from its fft.

Noise:

Gaussian noise is most common when observing natural processes

shot noise: bad individual samples (sporadic pops)

Drift noise: linked to time. where sensor becomes degraded

Filtering:

local averaging: local filters average of surrounding local values (use a copy buffer)

median average: gets rid of spot noise really quickly.

Winsorized Averaging: is a combination of median and averaging. It cuts off extreme values and then it averages.

convolution kernel filtering (2D): replacing my value with that of my neighbors. Can give different weights to different pixels/

kernel: 3×3 equal weights. can use it to detect edges etc. ( use imagej to write own filters)

gaussian: 7×7 pays less attention to corners.

Histograms: thresholding – determining foreground and background.

finding the best thresholding: use the random triangle method that usually works. eyeo thresholding is the intersection between different curves. iso thresholding.