30 June 1994 Morphological Hopfield nets
Author Affiliations +
The Hopfield network model associates an input pattern with trained patterns and is generally considered to be a pattern recognition system that completes missing pieces of the input image. In this paper the Morphological Hopfield Net associates segments in input patterns with trained pattern segments and is used to reconstruct known patterns degraded by noise by reconstructing the individual segments. A very simple Hopfield model is defined over an image space and consists of a large number of identical Hopfield networks, one about each pixel site, each with a local connectivity to a neighborhood of pixels. The weights are all 1 and the thresholds are adjusted to extreme values (max or min). It is shown that this Hopfield model is equivalent to a union of openings. Convergence occurs in only one iteration since the union of openings is idempotent.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen S. Wilson, Stephen S. Wilson, } "Morphological Hopfield nets", Proc. SPIE 2300, Image Algebra and Morphological Image Processing V, (30 June 1994); doi: 10.1117/12.179204; https://doi.org/10.1117/12.179204


Partial closing filters for image restoration
Proceedings of SPIE (March 28 1995)
Neural network method applied to particle-image velocimetry
Proceedings of SPIE (December 02 1993)
A new method for fast vehicle license plate detection
Proceedings of SPIE (December 08 2011)
Space adaptive wavelet packet image compression
Proceedings of SPIE (March 13 1996)

Back to Top