AbstractWe describe a system that performs model‐based recognition of the projections of generalized cylinders, and present new results on the final classification of the feature data. Two classification methods are proposed and compared. The first is a Bayesian technique that ranks the object space according to estimated conditional probability distributions. The second technique is a new feed‐forward “neural” implementation that utilizes the back‐propagation learning algorithm. The neural approach yields a 31.8 reduction in classification error for a database of twenty models relative to the Bayesian approach, although it does not provide an ordered ranking of the object space. The accuracy results of the neural approach represent a significant performance advance in feature‐based recognition by perceptual organization without the use of depth information. Examples are provided using the results of a simple segmentation system applied to real
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