Logarithmic Morphological Neural Nets robust to lighting variations
This work addresses robustness to lighting variations in image processing for applications like computer vision, but it is incremental as it builds on existing frameworks.
The paper tackled the problem of morphological neural networks lacking robustness to lighting variations by introducing a network based on Logarithmic Mathematical Morphology, which verifies robustness to such variations in images.
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.