Computer Science > Computer Vision and Pattern Recognition
[Submitted on 20 Apr 2022 (v1), last revised 29 Nov 2022 (this version, v2)]
Title:Logarithmic Morphological Neural Nets robust to lighting variations
View PDFAbstract: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.
Submission history
From: Guillaume Noyel [view email] [via CCSD proxy][v1] Wed, 20 Apr 2022 08:54:49 UTC (130 KB)
[v2] Tue, 29 Nov 2022 09:39:21 UTC (163 KB)
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