2 citations · 3 across the 6 of their papers we have counts for
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Moving Frame Net: SE(3)-Equivariant Network for Volumes
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer vision tasks, as they apply to datasets presenting…
Scale Equivariant U-Net
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
In neural networks, the property of being equivariant to transformations improves generalization when the corresponding symmetry is present in the data. In particular, scale-equiva…
Differential invariants for SE(2)-equivariant networks
Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1
Symmetry is present in many tasks in computer vision, where the same class of objects can appear transformed, e.g. rotated due to different camera orientations, or scaled due to pe…
Morphological adjunctions represented by matrices in max-plus algebra for signal and image processing
Samy Blusseau, Santiago Velasco-Forero, Jesus Angulo +1
In discrete signal and image processing, many dilations and erosions can be written as the max-plus and min-plus product of a matrix on a vector. Previous studies considered operat…