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Coarse-coded higher-order neural networks for PSRI object recognitionA higher-order neural network (HONN) can be designed to be invariant to changes in scale, translation, and inplane rotation. Invariances are built directly into the architecture of a HONN and do not need to be learned. Consequently, fewer training passes and a smaller training set are required to learn to distinguish between objects. The size of the input field is limited, however, because of the memory required for the large number of interconnections in a fully connected HONN. By coarse coding the input image, the input field size can be increased to allow the larger input scenes required for practical object recognition problems. We describe a coarse coding technique and present simulation results illustrating its usefulness and its limitations. Our simulations show that a third-order neural network can be trained to distinguish between two objects in a 4096 x 4096 pixel input field independent of transformations in translation, in-plane rotation, and scale in less than ten passes through the training set. Furthermore, we empirically determine the limits of the coarse coding technique in the object recognition domain.
Document ID
Document Type
Reprint (Version printed in journal)
External Source(s)
Spirkovska, Lilly (NASA Ames Research Center Moffett Field, CA, United States)
Reid, Max B. (NASA Ames Research Center Moffett Field, CA, United States)
Date Acquired
August 16, 2013
Publication Date
March 1, 1993
Publication Information
Publication: IEEE Transactions on Neural Networks
Volume: 4
Issue: 2
ISSN: 1045-9227
Subject Category
Distribution Limits