Connectionist networks for feature indexing and object recognition

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 [10] can be used to generate object hypotheses in parallel. We describe a framework for the connectionist implementation of such indexing and recognition techniques. With sucient 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.

• Type: Conference
• Year: 1996
• Where: CVPR
• Authors: Clark F. Olson
• Keywords: Computer Vision, Connectionist Network Formulations, CVPR 1996, Feature Indexing Techniques, Processing Elements