04 Mar 2013

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:




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.


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


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.