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arXiv · 2204.09319

Logarithmic Morphological Neural Nets robust to lighting variations

Guillaume Noyel·Emile Barbier--Renard·Michel Jourlin·Thierry Fournel
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Abstract

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.

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Guillaume Noyel, Emile Barbier--Renard, Michel Jourlin, Thierry Fournel. 2022-04-20. Logarithmic Morphological Neural Nets robust to lighting variations. https://doi.org/10.1007/978-3-031-19897-7_36

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