Nearest neighbor search with quadtrees

hi guys;
i'm interested in implementing an algorithm to watch for possible collisions between celestial planets...
this algorihm consists of checking for a given planet with coordinates(x,y) if some other planet in the system came closer than a given distance...this algorithm should perform better than the naive one which consists of cheking the distance against every other planet and which is o(n) (n is the number of planets in the system)
it would be preferable to use a quadtree for a nearest neighbor search algorithm to implement this collision watch algorithm...
please help me ! some sample code would be very welcomed
thanks

[url http://forum.java.sun.com/thread.jsp?thread=571601&forum=31]Cross-post.

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Hello everybody!
I am trying to upscale a movie from an old video game. The resolution is 320x240 and should be upscaled to 1440x1080. The problem is that I don't want Adobe Premiere Pro 6 to upscale it using a chroma subsampling method (http://ingomar.wesp.name/2011/04/dosbox-gameplay-video-capture.html), instead I want to use something similar to the option "nearest neigbor" (the one you have in Adobe Photoshop when you resize images). Why I want this is because I want to keep the pixels from the video game sharp. Is this possible to do?

And if you take a screen cap, import it into Photoshop and upscale by a factor 4 (with Nearest Neighbour) the result is amazing!
Actually, I find that up-rezzing with the Nearest Neighbor algorithm to be about the lowest quality of any of the algorithms. It came first, and is basically a holdover from about PS version 2.5. Bicubic interpolation was added later, and then Bicubic Smoother and Bicubic Sharper.
However, I am always working with continuous tone, high-rez digital photographs, and not screen-caps, so perhaps my material is not the ultimate to judge Nearest Neighbor?
Still, for a 16x increase, about the only thing that I can suggest (and this is for Stills, and not Video) would be Genuine Fractals (once Human Softaware, but acquired by another company). Still, that is beyond the max limit that I would be comfortable with.
Others have mentioned Red Giant's Magic Bullet Instant HD, and I would download the trial, then test. That might be "as good as it get."
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While on vacation in Colorado last week, we decided to head up to Crested Butte to see a local play production. My wife, searching for a local motel on a site I will not name, found one with great ratings that was quite reasonable and about "30 miles" away. Not being from Colorado, she went ahead and booked it, and then told me where it was. I shook my head. That is no where near 30 miles away, at least by road - it is on the other side of the mountain!
Obviously this company uses a point to point with nearest neighbor "as the crow flies" method. While simple points might work reasonably well for small areas, say in a city road grid, it is a horrible solution for larger areas and places like Colorado. With mountains that take hours to drive around, those 30 miles or so turn out to be 90 miles of road and take about 3 hours to drive based on posted speeds!
Needless to say, I was upset. After spending almost an hour on the phone to get this all straightened out and the bill credited, I thought I'd point this out. Not only for "buyer beware" - but mainly as a good example of what not to do when designing map-based systems for consumers. KISS is usually a good approach, but in this case it is a horrible one when a road network with speeds AKA network data model graph solution is required.
Bryan

Good point.
Thinking further, even in a city grid you have one-way streets, parks, longer blocks, etc., which can make the real distance (by road) much further than "as the crow flies". So I'm not sure nn is really good for any such "close to me" analysis tool.
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Hi guys;
I'm using a binary tree to construct a tree of two dimensional points .here is how i construct the tree :
the tree is constructed by successive subdivision of the Plan by semi-Lines accross a given point.
the first subdivision is done by say a horizantal line across a given point.the plan is then split into two regions : the upper and lower half plans. the points in the upper plan will be in the right subtree and those in the lower plan will go into the left subtree.
in the next step the plan will be subdiveded by a VERTICAL semi-line at this time : the points at the left of this line will be placed in the left subtree and those on the right will be placed to right subtree.
Now i managed to write an insert() method for this :
static Tree insert(Point p, Tree a, boolean vertical)
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if i pick a given point in the cloud ,i want to find the nearest neighbor of this point using the tree construct i described above.
is this possible ? How can I implement it ?
Many thanks for helping !

this is because i will be dealing with a verylarge
number of points so efficiency here is definitelya
crucial issue...what do you think josiah ?Well, I've used that little algorithm for a global
map with, say, 1e6 known points
and the response was always almost intantaneous. It
depends on the distribution
of your known points, i.e. you can easily draw
pathetic situations on a piece of
paper but on average the nearest point is found
withing a few searches (after
finding those two lines using an O(log(n)) binary
search ...
Two dimensional locality doesn't help you much when
you're using quad-trees;
as has been said before in this thread: two nearest
points may be miles apart
in the tree.
What does help (altough not much) is, after sorting
the points on increasing Y
values, do a minor sort using Gray-codes on the X
coordinate. But that's for a
later discussion ;-)
kind regards,
Jos
Well, I've used that little algorithm for a global
map with, say, 1e6 known points
and the response was always almost intantaneous. It
depends on the distribution
of your known points, i.e. you can easily draw
pathetic situations on a piece of
paper but on average the nearest point is found
withing a few searches (after
finding those two lines using an O(log(n)) binary
search ...
What you say is very encouraging so i will try your little algorithm for sure.
any way At this first stage of my application i'm not demanding some extra ordinary work to be acheived , and this one would be surely very sufficient..
Two dimensional locality doesn't help you much when
you're using quad-trees;
as has been said before in this thread: two nearest
points may be miles apart
in the tree.
you right !
What does help (altough not much) is, after sorting
the points on increasing Y
values, do a minor sort using Gray-codes on the X
coordinate. But that's for a
later discussion ;-)
something interesting i'll be patiently waiting for ;)
kind regards,
JosMany thanks ,

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Hello there, First time poster on the forums.
I've been looking into spatial comparison recently and have come across a few problems. The query I run takes 7 minutes to return the nearest neighbor.
My table that has the Geographical locations is of the following structure and has 1.7 million rows.
CREATE TABLE [dbo].[PostCodeData](
[OutwardCode] [varchar](4) NOT NULL,
[InwardCode] [varchar](3) NOT NULL,
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What I've tried:
1. Created a spatial index.
2. Followed a post somewhere on these forums about applying it as a hint (WITH: WITH (INDEX(ixs_postcode)))
It didn't let me apply the hint and gave the following error :
Query processor could not produce a query plan because of the hints defined in this query. Resubmit the query without specifying any hints and without using SET FORCEPLAN.
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Just before the end of the day yesterday, a colleague of mine spotted the missing 'Where' in the subquery and we added it in.
The query now looks as such :
UPDATE top(200) tbl_potatoes
SET PostCode =
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hours.
This is hardly going to be something that is going to work.
That was the update - the SELECT statement :
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,[Geolocation]
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from PostCodeData pc
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FROM [tbl_potatoes] pot
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K Nearest Neighbor Algorithm Code in Java

I am looking for a code in Java for K nearest neighbor algorithm (considering clusters if possible which is able to detect outlier clusters).
Thanks!
Edited by: win13 on Oct 1, 2007 11:54 AM
Edited by: win13 on Oct 1, 2007 12:00 PM

interface MS{ // metric space
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It is a mistake to assume that one particular definition of a cluster or an outlier will work in all cases. For example using the definition you supply, if you have one single mega cluster of a thousand elements located within a foot of one another here on earth and you have about 10 other "clusters', each with but a single element, each a single light year apart but all somewhere out near the andromeda galaxy, you will conclude that cluster centers are on the average about one light year apart but that there is one bad outlier, that one way over there on earth.
But, hey it's your data. Go nuts.
Just for the record, I don't typically test code that I type into the forum so what I have supplied may not even compile and certainly should not be assumed to be correct. You will need to read it, and treat it as an outline for the code that you will need to write.
Enjoy

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