Morphological Layers for Keras/Tensorflow2 The goal of morpholayers is to make the interactions between mathematical morphology and deep learning accessible for everyone.
If you find this code useful in your research, please consider citing:
@inproceedings{VelascoBMVC2022,
Author = {Velasco-Forero, S. and Rhim, A. and Angulo, J.},
Title = {Fixed Point Layers for Geodesic Morphological Operations},
Booktitle = {British Machine Vision Conference (BMVC)},
Year = {2022}
}
@article{VelascoSIAM2022,
author = {Velasco-Forero, Santiago and Pag\`{e}s, R. and Angulo, Jesus},
title = {Learnable Empirical Mode Decomposition based on Mathematical Morphology},
journal = {SIAM Journal on Imaging Sciences},
volume = {15},
number = {1},
pages = {23-44},
year = {2022},
}
Several examples of this library are available at: Examples
ECSIA mini-cours (Mathematical morphology meets Deep Learning)
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Santiago VELASCO-FORERO, Samy BLUSSEAU, Mateus SANGALLI
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MINES ParisTech, PSL Research University
Talks:
- Introduction
- Deep Learning in 15 minutes
- Mathematical morphology: Learning simple operators
- Depthwise Morphological Layers
- Morphological Scale-Spaces
Practical Sessions:
-Tutorial 0: Deep Learning in 15 minutes
Tutorial 1: Simple morphological operators using morpholayers
Tutorial 2: Learning morphological operators
Tutorial 3: Learning morphological layers in Fashion Mnist
Tutorial 4: Improving Max-Pooling layers using Dilations
Tutorial 5: Learning Additive Shift Equivariant Operators
Tutorial 6: Learning Scale-Equivariant Operators