Massively parallel model matching: geometric hashing on the Connection Machine

The parallelizability of geometric hashing is explored, and two algorithms are presented. Geometric hashing uses the collection of models in a preprocessing phase (executed off line) to build a hash table data structure. The data structure encodes the model information in a highly redundant, multiple-viewpoint way. During the recognition phase, when presented with a scene and extracted features, the hash table data structure indexes geometric properties of the scene features to candidate models. The first uses: parallel hypercube techniques to route information through a series of maps and building-block parallel algorithms. The second algorithm uses the Connection Machine´s large memory resources and achieves parallelism through broadcast facilities from the front end. The discussion is confined to the problem of recognizing dot patterson embedded in a scene after they have undergone translation, rotation, and scale changes.<>