Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Logarithmic Morphological Neural Nets robust to lighting variations   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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The categories of this paper are: chem-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc