Gaussian and sieve filters: Difference between revisions
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And there is more, something that has been thought about much less, let alone exploited - <span style="color:red;">a PhD project waiting to happen.</span> Sieves (and the recursive median filter is what I call one of the sieves) <span style="color:red;">are idempotent. In other words having made one pass through the data at any particular scale, making another pass through the result changes nothing.</span> <br><br> | And there is more, something that has been thought about much less, let alone exploited - <span style="color:red;">a PhD project waiting to happen.</span> Sieves (and the recursive median filter is what I call one of the sieves) <span style="color:red;">are idempotent. In other words having made one pass through the data at any particular scale, making another pass through the result changes nothing.</span> <br><br> | ||
This is | This is not like a linear (diffusion) filter where repeated passes at the same scale simply smooth the signal away. It means that one could build entirely new filtering schema, here are some [[new filtering schema| that are (for me) last minute.]] |
Revision as of 21:30, 6 August 2014
Siv1-gaussiansieve.png</wikiflv>
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And there is more, something that has been thought about much less, let alone exploited - a PhD project waiting to happen. Sieves (and the recursive median filter is what I call one of the sieves) are idempotent. In other words having made one pass through the data at any particular scale, making another pass through the result changes nothing.
This is not like a linear (diffusion) filter where repeated passes at the same scale simply smooth the signal away. It means that one could build entirely new filtering schema, here are some that are (for me) last minute.