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> They're also used in machine learning for 'feature extraction'... In this context the process is referred to more generally as "convolution" It's referred to
by blt 12y ago
> They're also used in machine learning for 'feature extraction'... In this context the process is referred to more generally as "convolution"
It's referred to as "convolution" in the image processing community too.
- raverbashing 12y agoNot surprising, because the kernel is convoluted on the pixels http://en.wikipedia.org/wiki/Convolution http://en.wikipedia.org/wiki/Convolution Now, to what's happening, you're basically applying a FIR filter to each pixel, so that each one depends also on the frequency information of adjacent pixels (in 2 dimensions) If someone wants to know more: http://en.wikipedia.org/wiki/Finite_impulse_response http://en.wikipedia.org/wiki/Finite_impulse_response http://en.wikipedia.org/wiki/Z-transform http://en.wikipedia.org/wiki/Z-transform
- darkmighty 12y agoThat's a much more profound explanation than the given. You can actually come up with values yourself then, and it reveals why cases like "edge detection" and "blur" work so nicely (edge detection approximates differentiation; blur acts as a low pass filter;...).
- lp251 12y agoMinor nitpicks- "Convolved" with the pixels. And the FIR filter doesn't depend on the frequency information in the adjacent pixels, but rather the intensity of the pixels. A short FIR filter must have large frequency support, so the filtering depends on the frequency information given by all pixels.
- ashmud 12y agoSAR Image Processor was my first exposure to convolution. http://www.general-cathexis.com/ http://www.general-cathexis.com/