# Logarithmic Morphological Neural Nets robust to lighting variations

arXiv: https://arxiv.org/abs/2204.09319 · HTML: https://mlsanity.com/paper/2204.09319/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Truly shift-invariant convolutional neural networks](https://mlsanity.com/paper/2011.14214/) (2020)
- [Improving Shadow Suppression for Illumination Robust Face Recognition](https://mlsanity.com/paper/1710.05073/) (2017)
- [Detail-Preserving Pooling in Deep Networks](https://mlsanity.com/paper/1804.04076/) (2018)
- [Morphological Network: How Far Can We Go with Morphological Neurons?](https://mlsanity.com/paper/1901.00109/) (2019)
- [Deep Morphological Neural Networks](https://mlsanity.com/paper/1909.01532/) (2019)
- [Max-plus Operators Applied to Filter Selection and Model Pruning in Neural Networks](https://mlsanity.com/paper/1903.08072/) (2019)
- [An Introduction to Deep Morphological Networks](https://mlsanity.com/paper/1906.01751/) (2019)
- [Going beyond p-convolutions to learn grayscale morphological operators](https://mlsanity.com/paper/2102.10038/) (2021)
- [Double-sided probing by map of Asplund's distances using Logarithmic Image Processing in the framework of Mathematical Morphology](https://mlsanity.com/paper/1701.08092/) (2017)
- [Logarithmic mathematical morphology: a new framework adaptive to illumination changes](https://mlsanity.com/paper/1806.02998/) (2018)

## Built on by (most cited first, 1 of 1)

- [Logarithmic Mathematical Morphology: Theory and applications](https://mlsanity.com/paper/2309.02007/) (2023)

## Abstract (arXiv)

Morphological neural networks allow to learn the weights of a structuring function knowing the desired output image. However, those networks are not intrinsically robust to lighting variations in images with an optical cause, such as a change of light intensity. In this paper, we introduce a morphological neural network which possesses such a robustness to lighting variations. It is based on the recent framework of Logarithmic Mathematical Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic Image Processing (LIP) model. This model has a LIP additive law which simulates in images a variation of the light intensity. We especially learn the structuring function of a LMM operator robust to those variations, namely : the map of LIP-additive Asplund distances. Results in images show that our neural network verifies the required property.