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A Massively Parallel Approach To Object Recognition

This paper describes a model-based vision system for recognizing three-dimensional objects in two-dimensional images of natural scenes. The system is implemented entirely in parallel on a CM-2 Connection Machine System. It is novel in that it does not use a three-dimensional representation of the object. Instead, the representation of the object is constructed from a number of images of the object taken from known positions. By interpolating the appearance of the same feature in different views, it is possible to estimate the appearance of the feature from any camera position. By applying this interpolation process to many features, it is possible to reconstruct the appearance of the object from any angle and position. Features observed in the scene to be recognized can then be used to generate hypotheses as to the pose of the object. These hypotheses then compete to explain the observed features of the scene. A novel representation of features is used, which permits features of many types - lines, corners, curvature extrema and inflection points - to be treated identically by the correspondence, interpolation, and hypothesis generation processes.