Learning in Interpolation Networks for Irregular Sampling: Some Convergence Properties

Albert J. Ahumada; Jeffrey B. Mulligan

doi:10.1364/AV.1989.WC3

Applied Vision
Technical Digest Series (Optica Publishing Group, 1989),
paper WC3
•https://doi.org/10.1364/AV.1989.WC3

Learning in Interpolation Networks for Irregular Sampling: Some Convergence Properties

Albert J. Ahumada and Jeffrey B. Mulligan

Applied Vision 1989

San Francisco, California United States
12–14 July 1989
ISBN:

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Spatial and Temporal Image Sampling and Display (WC)

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A. J. Ahumada and J. B. Mulligan, "Learning in Interpolation Networks for Irregular Sampling: Some Convergence Properties," in Applied Vision, Technical Digest Series (Optica Publishing Group, 1989), paper WC3.

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Abstract

Recently, Ahumada and Yellott (1) and Maloney (5,6) have presented schemes for training networks designed to reconstruct irregularly sampled retinal images. In these schemes adjustable weighting networks provide compensation for the irregularities in the retinal array and the geometrical distortions in intermediate pathways. This paper presents some ideas relating to the convergence of the training algorithms.

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