Gaussian and sieve filters: Difference between revisions
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The output depends on the computation kernel the recursive median is one. Others include connected set openings ('o'), closings ('c') and composites of these 'M' ('o' followed by 'c' at each stage) or 'N' ('c' followed by 'o'). These last two yield outputs that are almost indistinguishable from the recursive median. (I have just realised how confusing it is referring to the recursive mean as an 'm' sieve, I shall start using the abbreviation 'v' as much as possible instead.) This is dull stuff.<br><br> | The output depends on the computation kernel the recursive median is one. Others include connected set openings ('o'), closings ('c') and composites of these 'M' ('o' followed by 'c' at each stage) or 'N' ('c' followed by 'o'). These last two yield outputs that are almost indistinguishable from the recursive median. (I have just realised how confusing it is referring to the recursive mean as an 'm' sieve, I shall start using the abbreviation 'v' as much as possible instead.) This is dull stuff.<br><br> | ||
This is dull stuff <span style="color:#C46210;"> compared to something that has been thought about much less, let alone exploited</span>- <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 dull stuff <span style="color:#C46210;"> compared to something that has been thought about much less, let alone exploited</span>- <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 not like a linear (diffusion) filter where repeated passes at the same scale simply smooth the signal away. It means that one <span style="color:#C46210;">could build entirely new filtering schema</span>, here are some [[new filtering schema| ideas.]] They begin to look a little biological. | This is not like a linear (diffusion) filter where repeated passes at the same scale simply smooth the signal away. It means that one <span style="color:#C46210;">could build entirely new filtering schema</span>, here are some [[new filtering schema| ideas.]] They begin to look a little biological. |
Revision as of 21:49, 6 August 2014
Siv1-gaussiansieve.png</wikiflv>
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The output depends on the computation kernel the recursive median is one. Others include connected set openings ('o'), closings ('c') and composites of these 'M' ('o' followed by 'c' at each stage) or 'N' ('c' followed by 'o'). These last two yield outputs that are almost indistinguishable from the recursive median. (I have just realised how confusing it is referring to the recursive mean as an 'm' sieve, I shall start using the abbreviation 'v' as much as possible instead.) This is dull stuff.
This is dull stuff compared to 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 ideas. They begin to look a little biological.