An algorithm that computes the best matching of two trees is described. The degree of mismatch, i.e., the distance, is measured in terms of the number of node splitting and merging operations required. The proposed tree distance is a more appropriate measurement of structural defonnation than the tree distance measure in terms of the number of insertions, deletions, and substitutions of tree nodes, as defined in previous studies. An algorithm that uses a divide-and-conquer strategy is presented. The analysis shows that the time complexity is O(NM2) where N and Al are the number of nodes of the two trees, respectively. The algorithm has been implemented on a VAX 11/780.

An image segmentation algorithm based on histogram clustering and probabilistic relaxation labeling is explored. The algorithm is evaluated by means of a set of artificially generated test images with known parameters. Two sources of pixel labeling errors are revealed. The first derives from distribution overlap in the histogram and leads to fragmented or missing regions in a segmentation. The second derives from the gloal nature of the compatibility coefficients used in the relaxation process. The coefficients are shown to be insufficient to correct certain labeling errors and can even cause the destruction of fine image details during the course of the relaxation updating process. A potential solution to these problems is shown to be obtainable by using orientation dependent compatibility coefficients and localizing the scope of the algorithm to small subimages followed by a merging of the segmented subimages.