The original description of the k -d tree recognized that rebalancing techniques, such as are used to build an AVL tree or a red-black tree, are not applicable to a k -d tree. Hence, in order to build a balanced k -d tree, it is necessary to obtain all of the data prior to building the tree then to build the tree via recursive subdivision of the data. One algorithm for building a balanced k -d ...

in order to investigate variations in plant diversity in dr. dorostkar's forest reservoir, located in gisoum talesh, 44 sampling plots were applied by the randomized-systematic method. the diameter of all tree species (dbh?5 cm) was measured in 400 m2 (20×20 m) square sampling plots. in addition, the number of individuals of shrubs and saplings (dbh?5 cm) were enumerated in circular 100 m2...

We present two new d-dimensional data structures, called the PKD-tree and the PKD+-tree, respectively. They are explored for indexing combined text and point data in low and high dimensional data spaces, and evaluated experimentally for orthogonal range search (for 2 ≤ d ≤ 128 and n up to 1,000,000) using synthetic data points and real data. The experimental results show that the PKD-tree and t...

A method is presented that uses an Approximate Nearest Neighbor method for determining correspondences within the Iterative Closest Point Algorithm. The method is based upon the k-d tree. The standard k-d tree uses a tentative backtracking search to identify nearest neighbors. In contrast, the Approximate k-d tree (Ak-d tree) applies a depthfirst nontentative search to the k-d tree structure. T...