|
|
FAST K-NEAREST NEIGHBOURS SEARCH 3D VERSION 1.0 File ID: 78805 |
|---|
|
| FAST K-NEAREST NEIGHBOURS SEARCH 3D VERSION 1.0 License: Freeware File Size: 10.0 KB Downloads: 87 | User Rating: |  | (1 rating) |
| Submit Rating: |
|
|
|
FAST K-NEAREST NEIGHBOURS SEARCH 3D VERSION 1.0 Description |
|---|
Description: In this file you can find a simple but very effective algorithm for Nearest Neighbour Search which I megalomaniacly called the GLTree.
You want more? go to the Professional version of GLTree
It has been designed for uniformly random data, where is the fastest I ever used, but works fine even on sparse ones. If points are too sparse, for example logspace data, search is still performed correctly but speed can degenerate to a brute search algorithm. In these cases a different data structure is needed but for lack of time I havend-deOaot still coded. If query points are close to reference it is very efficient on sparse dataset too.
The tree can be build without running any search. The pointer passed to workspace can be used for the above routines. The tree costruction has linear time complexity and it is very fast, so it becomes advantageus against brute search even for a small number of points. Id-deOaod like to point that in GL-Tree searching has linear complexity (on uniform dataset) instead of n*log(n) like in kd-tree.
It is possible to choose if return the distances.
<!--dp_youtube_begin:http://www.youtube.com/watch?v=mJ2ztIDGVNU&feature=player_embedded--><object width="425" height="344"><param name="movie" value="http://www.youtube.com/v/mJ2ztIDGVNU&hl=ru&fs=1"></param><param name="wmode" value="transparent" /><param name="allowFullScreen" value="true"></param><param name="allowscriptaccess" value="always"></param><embed src="http://www.youtube.com/v/mJ2ztIDGVNU&hl=ru&fs=1" type="application/x-shockwave-flash" allowscriptaccess="always" allowfullscreen="true" wmode="transparent" width="425" height="344"></embed></object><!--dp_youtube_end--> License: Freeware Related: Build, be used, advantageus, Designed, are too, Close, Algorithm, and it, Search, brute search, can be, degenerate, Find, called, Works, but for, Speed, but very O/S:BSD, Linux, Solaris, Mac OS X File Size: 10.0 KB Downloads: 87
|
|
| More Similar Code |
|---|
This is just a brute force implementation of k nearest neighbor search without using any fancy data structure, such as kd-tree. However it is the fastest knn matlab implementation I can find.
A partial sort mex function is implemented which is a simple wrapper of c++ partial_sort.
Provided the sort function, the matlab code is only of two lines. However, it is extremely fast.
install:
build;
This is a small but efficient tool to perform K-nearest neighbor search, which has wide Science and Engineering applications, such as pattern recognition, data mining and signal processing.
The code was initially implemented through...
this file you can find a simple but very effective algorithm for Nearest Neighbour Search which I megalomaniacly called the GLTree.
You want more? go to the Professional version of GLTree
It has been designed for uniformly...
This implementation includes modules for radiometric enhancement of colored 3D point clouds and nearest neighbor search. The introduced method detects the overlapping parts and derives a transformation function that reduces radiometric...
A GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc.
A GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc.
A fast artificial intelligence library which currently supports:
kNN (k-Nearest Neighbor algorithm)
MLP (Multilayer-Perceptron)
This is the 3D version of the earlier 2D version. It will be used for contour-guided deformation reg
Fuzzy k-nearest neighbors classifier that can work with training samples whose label info is fuzzified. The prototype is as follows.
[y,predict_class] = f_knn(tr,tr_memberships,te,k)
tr: training samples
tr_memberships:...
3D version of traditional four-on-one-row game. Computer opponent can play at 4 levels. |
| User Review for FAST K-NEAREST NEIGHBOURS SEARCH 3D VERSION |
|
