Scale equivariant neural networks with morphological scale-spaces

DOI10.1007/978-3-030-76657-3_35zbMath1484.68304arXiv2105.01335OpenAlexW3165455669MaRDI QIDQ2061842

Jesús Angulo, Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero

Publication date: 21 December 2021

Full work available at URL: https://arxiv.org/abs/2105.01335

zbMATH Keywords

neural networksscale equivariancemorphological scale-space

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05)

Related Items (3)

Functional properties of PDE-based group equivariant convolutional neural networks ⋮ Can generalised divergences help for invariant neural networks? ⋮ Learnable Empirical Mode Decomposition based on Mathematical Morphology

Cites Work

• Mathematical morphology on irregularly sampled data applied to segmentation of 3D point clouds of urban scenes

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