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May 20, 2015 at 23:30 comment added zQAycX @kodlu I'm willing to accept this as an answer if you can suggest a few search terms or references to help in understanding the design of spatial filters. If it helps, I've added a few observations about $r(\mathbf{x^\prime},z)$ and $LC(x,y)$ to the postscript. Thanks to the moderator for not closing the discussion down.
May 20, 2015 at 1:50 comment added kodlu @zQAycX, yes I would suspect both to be true, but I have no proof.
May 19, 2015 at 22:05 comment added zQAycX I've edited the question to formalize it somewhat. If I take you at your word that they have to behave that way, then: $LC(x,y)$ is effectively random and needs a search for every value of $x,y$. Also it's likely that $r(x,z)$ has too many localized maxima and finding the absolute maxima is inefficient. Would you suspect both to be true?
May 19, 2015 at 13:17 comment added tylo "otherwise they have to behave like functions that depend on brute force searching" That's the part telling you how (in)efficient it is. And brute search is very, very slow, compared to state-of-the-art factorization algorithms.
May 19, 2015 at 11:05 comment added zQAycX Thanks. Your first and last points are clear. A search for the single answer in a raster image is difficult, so the question that remains is 'how complex is the horribly noisy continuous function?' In the plots I have seen, the filtered image has cone-like 'teeth' that rise at roughly the same rate, meaning that their openings (at a nominal height Z) as measured normal to the curve of the 'gumline' depend on their depth. Unfortunately the math is beyond me and better understanding your second point above would convince me that the pursuit would be futile.
May 19, 2015 at 1:00 history answered kodlu CC BY-SA 3.0