The Perceptron Algorithm: Image and Signal Decomposition, Compression, and Analysis by Iterative Gaussian Blurring

TL;DR

The paper introduces the Perceptron Algorithm, a novel method for tunable image and signal compression, analysis, and pattern recognition, with broad applications in imaging, biomedical fields, and neural computation.

Contribution

It presents a new algorithm that leverages simple concepts in a novel way for highly efficient compression and analysis, opening new avenues in computational science and technology.

Findings

Achieves orders of magnitude improvement in image compression

Enables detailed multilayered image and signal analysis

Potential applications in biomedical imaging and neural computation

Abstract

A novel algorithm for tunable compression to within the precision of reproduction targets, or storage, is proposed. The new algorithm is termed the `Perceptron Algorithm', which utilises simple existing concepts in a novel way, has multiple immediate commercial application aspects as well as it opens up a multitude of fronts in computational science and technology. The aims of this paper are to present the concepts underlying the algorithm, observations by its application to some example cases, and the identification of a multitude of potential areas of applications such as: image compression by orders of magnitude, signal compression including sound as well, image analysis in a multilayered detailed analysis, pattern recognition and matching and rapid database searching (e.g. face recognition), motion analysis, biomedical applications e.g. in MRI and CAT scan image analysis and compression, as well as hints on the link of these ideas to the way how biological memory might work leading to new points of view in neural computation. Commercial applications of immediate interest are the compression of images at the source (e.g. photographic equipment, scanners, satellite imaging systems), DVD film compression, pay-per-view downloads acceleration and many others identified in the present paper at its conclusion and future work section.