%% 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%