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Connectionist Networks for Feature Indexing and Object Recognition
Clark F. Olson In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 907-912, 1996. Download (210 K) Feature indexing techniques are promising for object recognition since they can quickly reduce the set of possible matches for a set of image features. This work exploits another property of such techniques. They have inherently parallel structure and connectionist network formulations are easy to develop. Once indexing has been performed, a voting scheme such as geometric hashing [Lamdan, Schwartz, and Wolfson, 1990] can be used to generate object hypotheses in parallel. We describe a framework for the connectionist implementation of such indexing and recognition techniques. With sufficient processing elements, recognition can be performed in a small number of time steps. The number of processing elements necessary to achieve peak performance and the fan-in/fan-out required for the processing elements is examined. These techniques have been simulated on a conventional architecture with good results. |
