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Object recognition by fast hypothesis generation and reasoning about object interactions

We present a two-step approach for recognizing multiple 3-D objects in single 2-D images. In the first step, hypotheses of object instances are generated using a memory-based technique. This technique relies on an array, which is computed off-line, that associates a large number of object poses with corresponding image features. During actual recognition, the array serves as a discrete approximation of the inverse projection function, and each image feature returns a set of poses that are accumulated by a generalized Hough transform. In the second step, the configuration of hypotheses that best interprets the image is calculated using a Bayesian network. The network represents both visual effects, such as the creation and occlusion of image features, and physical constraints, such as object interference.