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Scale-Space Theory in Computer Vision

. The Springer International Series in Engineering and Computer Science Springer, (1994)

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Provably scale-covariant hierarchical continuous networks based on scale-normalized differential expressions coupled in cascade.. CoRR, (2019)Scale-Covariant and Scale-Invariant Gaussian Derivative Networks.. SSVM, volume 12679 of Lecture Notes in Computer Science, page 3-14. Springer, (2021)Covariance properties under natural image transformations for the generalised Gaussian derivative model for visual receptive fields.. Frontiers Comput. Neurosci., (February 2023)Discrete derivative approximations with scale-space properties: A basis for low-level feature extraction.. J. Math. Imaging Vis., 3 (4): 349-376 (1993)Provably Scale-Covariant Networks from Oriented Quasi Quadrature Measures in Cascade.. SSVM, volume 11603 of Lecture Notes in Computer Science, page 328-340. Springer, (2019)In Memoriam: Jan-Olof Eklundh., , , , , , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 44 (9): 4488-4489 (2022)Exploring the ability of CNN s to generalise to previously unseen scales over wide scale ranges., and . ICPR, page 1181-1188. IEEE, (2020)A time-causal and time-recursive analogue of the Gabor transform.. CoRR, (2023)Covariance properties under natural image transformations for the generalized Gaussian derivative model for visual receptive fields.. CoRR, (2023)Inability of spatial transformations of CNN feature maps to support invariant recognition., , , and . CoRR, (2020)