Issue No. 01 - January (1985 vol. 7)
Hanan Samet , Department of Computer Science, University of Maryland, College Park, MD 20742.
Many standard image processing operations can be implemented using quadtrees as a simple tree traversal where, at each terminal node, a computation is performed involving some of that node's neighbors. Most of this work has involved the use of bottom-up neighbor-finding techniques which search for a nearest common ancestor. Recently, top-down techniques have been proposed which make use of a neighbor vector as the tree is traversed. A simplified version of the top-down method for a quadtree in the context of a general-purpose tree traversal algorithm is presented. It differs, in part, from prior work in its ability to compute diagonally adjacent neighbors rather than just horizontally and vertically adjacent neighbors. It builds a neighbor vector for each node using a minimal amount of information. Analysis of the algorithm shows that its execution time is directly proportional to the number of nodes in the tree. However, it does require some extra storage. Use of the algorithm leads to lower execution time bounds for some common quadtree image processing operations such as connected component labeling.
Hanan Samet, "A Top-Down Quadtree Traversal Algorithm", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 7, no. , pp. 94-98, January 1985, doi:10.1109/TPAMI.1985.4767622