A neural-based scheme for pattern recognition and construction of three-dimensional images from partial cues is presented. Clusters of neural nets operating concurrently are used to learn and recall patterns at X, Y, Z planes. Simulations are performed based on a Hopfield-like network to investigate the effect of learning trials and firing percentage of nodes per cycle. A method for computing the common features of input patterns is given. Results showed that maximum parallel processing can be achieved.
|Publication status||Published - 1987 Dec 1|
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