  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.
