📄 kdtree.m
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%% KDTREE Find closest points using a k-D tree.% % CP = KDTREE( REFERENCE, MODEL ) finds the closest points in% REFERENCE for each point in MODEL. The search is performed in an% efficient manner by building a k-D tree from the datapoints in% REFERENCE, and querying the tree for each datapoint in% MODEL. %% Input :% REFERENCE is an NxD matrix, where each row is a D-dimensional% point. MODEL is an MxD matrix, where each row is a D-dimensional% query point. %% Output:% CP is the same dimension as MODEL. There is a one-to-one% relationship between the rows of MODEL and the rows of CP. The% i-th row (point) of CP is a row (point) from REFERENCE which% is closest to the i-th row (point) of MODEL. The "closest"% metric is defined as the D-dimensional Euclidean (2-norm)% distance.%% % [CP, DIST] = KDTREE( ... ) returns the distances between% each row of MODEL and its closest point match from the k-D tree% in the vector DIST. DIST(i) corresponds to the i-th row (point)% of MODEL.%% The default behavior of the function is that the k-D tree is% destroyed when the function returns. If you would like to save% the k-D tree in memory for use at a later time for additional% queries on the same REFERENCE data, then call the function with% an additional output:%% [CP, DIST, ROOT] = KDTREE(REFERENCE, MODEL) where ROOT% receives a pointer to the root of the k-D tree.%% Subsequently, use the following call to pass the k-D tree back% into the mex function:%% [CP, DIST, ROOT] = KDTREE([], MODEL, ROOT)% % Note that ROOT is again an output, preventing the tree from% being removed from memory. %% Ultimately, to clear the k-D tree from memory, pass ROOT as% input, but do not receive it as output:%% KDTREE([], [], ROOT)%% New since June 2004: This k-D tree library now handles points% with dimension greater than 3.%% See also KDTREEIDX and KDRANGEQUERY.% % Written by / send comments or suggestions to :% Guy Shechter% guy at jhu dot edu% June 2004%
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