Related papers: Going beyond p-convolutions to learn grayscale morphological operators

Going beyond p-convolutions to learn grayscale morphological operators

URL: http://arxiv.org/abs/2102.10038v1
Date: Fri, 19 Feb 2021 17:22:16 GMT
Title: Going beyond p-convolutions to learn grayscale morphological operators
Authors: Alexandre Kirszenberg, Guillaume Tochon, Elodie Puybareau and Jesus Angulo
Abstract summary: We present two new morphological layers based on the same principle as the p-convolutional layer. In this work, we present two new morphological layers based on the same principle as the p-convolutional layer.
Score: 64.38361575778237
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with erosions or dilations is particularly challenging because the min and max operations are not differentiable. Relying on the asymptotic behavior of the counter-harmonic mean, p-convolutional layers were proposed as a possible workaround to this issue since they can perform pseudo-dilation or pseudo-erosion operations (depending on the value of their inner parameter p), and very promising results were reported. In this work, we present two new morphological layers based on the same principle as the p-convolutional layer while circumventing its principal drawbacks, and demonstrate their potential interest in further implementations within deep convolutional neural network architectures.

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