Learning the Hit-or-Miss Transform-Based Morphological Neural Networks

Learning the Hit-or-Miss Transform-Based Morphological Neural Networks

Mathematical morphology is well suited for learning interpretable shapes because of its structure-based operations. In this article, we propose techniques to improve the learning of the hit-or-miss transform, a morphological operation developed to detect object shapes by simultaneously matching the...

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Bibliographic Details
Main Authors:	Muhammad Aminul Islam, Sireesha Chimbili
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Series:	IEEE Access
Subjects:	Convolutional neural network hit-or-miss transform mathematical morphology morphological neural network
Online Access:	https://ieeexplore.ieee.org/document/10960390/
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