Hardware-implementable neural network for rotation-scaling invariant pattern classification

Rafael M. Inigo; Catherine Q. Xu; Begona C. Arrue; Eugene S. McVey

doi:10.1117/12.60033

1 July 1992 Hardware-implementable neural network for rotation-scaling invariant pattern classification

Rafael M. Inigo, Catherine Q. Xu, Begona C. Arrue, Eugene S. McVey

Author Affiliations +

Journal of Electronic Imaging, Vol. 1, Issue 3, (July 1992). https://doi.org/10.1117/12.60033

Abstract

The design, hardware implementation, and simulation of a shift invariant pattern recognizer based on a modified higher order neural network (MHONN) is presented. When the MHONN is integrated with centroid calculation and logarithmic spiral mapping subsystems, translation, rotation around the optical axis, and scaling invariant pattern recognition can be achieved. The design objective is to deal with large-scale images with possible pattern deformation, noise, and highly textured backgrounds. Images are acquired with a 256 x 256 infrared sensor. We describe the theory of the MHONN, its hardware implementation, and simulation results.

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Rafael M. Inigo, Catherine Q. Xu, Begona C. Arrue, and Eugene S. McVey "Hardware-implementable neural network for rotation-scaling invariant pattern classification," Journal of Electronic Imaging 1(3), (1 July 1992). https://doi.org/10.1117/12.60033

Published: 1 July 1992

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