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

arXiv: https://arxiv.org/abs/1911.11251 · HTML: https://mlsanity.com/paper/1911.11251/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [HexCNN: A Framework for Native Hexagonal Convolutional Neural Networks](https://mlsanity.com/paper/2101.10897/) (2021)
- [Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects](https://mlsanity.com/paper/2407.20268/) (2024)
- [Biologically Inspired Hexagonal Deep Learning for Hexagonal Image Generation](https://mlsanity.com/paper/2101.00337/) (2021)
- [Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System](https://mlsanity.com/paper/2204.11970/) (2022)
- [Biologically inspired deep residual networks for computer vision applications](https://mlsanity.com/paper/2205.02551/) (2022)

## Abstract (arXiv)

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.