A robust parallel implementation of 2D model-based recognition

An approach to 2D model-based object recognition is developed, suitable for implementation on a highly parallel SIMD (single-instruction, multiple data stream) computer. Object models and image data are represented as contour features. Transformation sampling is used to determine the optimal model-feature-to-image-feature transformation by sampling the space of possible transformations. Only a small part of this space need actually be sampled due to the constraints placed on transformations by individual matches of image features to model features. The procedure requires O(Kmn) processors and O(log² (Kmn)) time, where m is the number of model features, n is the number of image features, and K depends on the size of the image. The procedure works well and is extremely robust in the presence of occlusion. An implementation of the procedure on the Connection Machine is described, and some experimental results given