Re:Image Kernels explained visually
by userbinator on Monday August 08, 2016 @11:11PM
(#12252346)
To put it succinctly, convolution just is a fancy term for a weighted average, and a kernel is the name for a set of weights.
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In general terms, convolution is defined for two functions which both can be continuous and then it's essentially an "weighted integral". In 1D case (sound), when both functions have discrete domain and domain of filter is finite, then it is the same thing as FIR filter. Convolution with continuous domain kernel (eg. sinc()) is useful for example for resampling/scaling.