Hexagonal Image Processing in the Context of Machine Learning: Conception of a Biologically Inspired Hexagonal Deep Learning Framework

TL;DR

This paper introduces Hexnet, a biologically inspired hexagonal image processing framework that improves upon existing systems by reducing parameters and enhancing training efficiency in deep neural networks.

Contribution

It presents a novel hexagonal deep learning framework, Hexnet, and demonstrates its advantages over traditional square lattice-based methods in image processing and neural network training.

Findings

Hexnet surpasses current hexagonal image processing approaches.

Hexagonal neural networks benefit from reduced parameters.

Training and testing rates are increased with hexagonal architectures.

Abstract

Inspired by the human visual perception system, hexagonal image processing in the context of machine learning deals with the development of image processing systems that combine the advantages of evolutionary motivated structures based on biological models. While conventional state-of-the-art image processing systems of recording and output devices almost exclusively utilize square arranged methods, their hexagonal counterparts offer a number of key advantages that can benefit both researchers and users. This contribution serves as a general application-oriented approach the synthesis of the therefore designed hexagonal image processing framework, called Hexnet, the processing steps of hexagonal image transformation, and dependent methods. The results of our created test environment show that the realized framework surpasses current approaches of hexagonal image processing systems, while hexagonal artificial neural networks can benefit from the implemented hexagonal architecture. As hexagonal lattice format based deep neural networks, also called H-DNN, can be compared to their square counterparts by transforming classical square lattice based data sets into their hexagonal representation, they can also result in a reduction of trainable parameters as well as result in increased training and test rates.