Scale-covariant and scale-invariant Gaussian derivative networks

DOI10.1007/978-3-030-75549-2_1zbMath1484.68207OpenAlexW3160801577MaRDI QIDQ5896859

Tony Lindeberg

Publication date: 20 December 2021

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303875

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computing methodologies for image processing (68U10)

Related Items (2)

Scale-invariant scale-channel networks: deep networks that generalise to previously unseen scales ⋮ Scale-covariant and scale-invariant Gaussian derivative networks

Cites Work

• Unnamed Item
• A computational theory of visual receptive fields
• The structure of images
• Generalized Gaussian scale-space axiomatics comprising linear scale-space, affine scale-space and spatio-temporal scale-space
• Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade
• Image matching using generalized scale-space interest points

This page was built for publication: Scale-covariant and scale-invariant Gaussian derivative networks