# Human eye inspired log-polar pre-processing for neural networks

arXiv: https://arxiv.org/abs/1911.01141 · HTML: https://mlsanity.com/paper/1911.01141/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Policy-based Foveated Imaging and Perception](https://mlsanity.com/paper/2606.02565/) (2026)
- [Foveated Retinotopy Improves Classification and Localization in Convolutional Neural Networks](https://mlsanity.com/paper/2402.15480/) (2024)

## Abstract (arXiv)

In this paper we draw inspiration from the human visual system, and present a bio-inspired pre-processing stage for neural networks. We implement this by applying a log-polar transformation as a pre-processing step, and to demonstrate, we have used a naive convolutional neural network (CNN). We demonstrate that a bio-inspired pre-processing stage can achieve rotation and scale robustness in CNNs. A key point in this paper is that the CNN does not need to be trained to identify rotation or scaling permutations; rather it is the log-polar pre-processing step that converts the image into a format that allows the CNN to handle rotation and scaling permutations. In addition we demonstrate how adding a log-polar transformation as a pre-processing step can reduce the image size to ~20% of the Euclidean image size, without significantly compromising classification accuracy of the CNN. The pre-processing stage presented in this paper is modelled after the retina and therefore is only tested against an image dataset. Note: This paper has been submitted for SAUPEC/RobMech/PRASA 2020.