Approximating morphological operators with part-based representations learned by asymmetric auto-encoders

DOI10.1515/mathm-2020-0102zbMath1469.68153OpenAlexW3055580303MaRDI QIDQ1980892

Jesús Angulo, Samy Blusseau, Bastien Ponchon, Santiago Velasco-Forero, Isabelle Bloch

Publication date: 9 September 2021

Published in: Mathematical Morphology. Theory and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/mathm-2020-0102

zbMATH Keywords

mathematical morphologyrepresentation learningauto-encodersmorphological invariancenon-negative sparse codingXAI

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computing methodologies for image processing (68U10) Computer graphics; computational geometry (digital and algorithmic aspects) (68U05) Computational aspects of data analysis and big data (68T09)

Uses Software

• MNIST
• Fashion-MNIST
• InfoGAN

Cites Work

• Max-plus operators applied to filter selection and model pruning in neural networks
• Part-based approximations for morphological operators using asymmetric auto-encoders
• Nonredundant sparse feature extraction using autoencoders with receptive fields clustering
• Sparse Modeling for Image and Vision Processing
• Sparse Mathematical Morphology Using Non-negative Matrix Factorization
• Morphological Perceptrons: Geometry and Training Algorithms
• Unnamed Item
• Unnamed Item

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