from
k-D tree
by Guy Shechter
Perform closest point search or range query using a k-D tree implementation.
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| kdrangequery.m |
%
% KDRANGEQUERY Find all points within a local neighborhood.
%
% PTS = KDRANGEQUERY( ROOT, QUERYPT, DISTLIM ) finds all the
% points stored in the k-D tree ROOT that are within DISTLIM units
% from the QUERYPT. Proximity is quantified usng a D-dimensional
% Euclidean (2-norm) distance.
%
% Input :
% ROOT is a pointer to a k-D tree which must be constructed with
% the function KDTREE or KDTREEIDX. QUERYPT is a 1xD vector
% representing a point in D-dimensional space. DISTLIM is a
% scalar which specifies the radius of the neighborhood around
% QUERYPT.
%
% Output:
% PTS is an NxD matrix, where each row is a datapoint from the
% k-D tree ROOT. Each of these datapoints is found within a
% distance DISTLIM from QUERYPT.
%
% [PTS, DIST] = KDRANGEQUERY( ... ) returns the distances between
% each row of PTS and QUERYPT in the Nx1 vector DIST.
%
% [PTS, DIST, IDX ] = KDRANGEQUERY( ... ) returns the index value
% for each row (point) of PTS. The index value maps back to a row
% from the matrix REFERENCE used to build the k-D tree (see the
% KDTREE or KDTREEIDX functions).
%
% Limitations:
% QUERYPT must be a 1xD dimensional array meaning that the range
% query can be performed for one point at a time.
%
% See also KDTREE and KDTREEIDX.
%
% Written by / send comments or suggestions to :
% Guy Shechter
% guy at jhu dot edu
% June 2004
%
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