Artikel
Open Access
Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions
-
Teo Asplund
,
Cris L. Luengo Hendriks
Veröffentlicht/Copyright:
28. November 2020
Received: 2019-10-24
Accepted: 2020-11-02
Published Online: 2020-11-28
© 2020 Teo Asplund et al., published by De Gruyter
Artikel in diesem Heft
- Regular articles
- Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains
- Image Restoration by Learning Morphological Opening-Closing Network
- Digital Objects in Rhombic Dodecahedron Grid
- Special Issue: ISMM 2019
- Editorial — Special Issue: ISMM 2019
- Hyperspectral Image Classification Based on Mathematical Morphology and Tensor Decomposition
- Boundary Morphology for Hierarchical Simplification of Archaeological Fragments
- Approximating morphological operators with part-based representations learned by asymmetric auto-encoders
- Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions
- Fast marching based superpixels
Schlagwörter für diesen Artikel
Mathematical morphology;
irregular sampling;
adaptive morphology;
non-flat morphology;
elliptical structuring elements;
local structure tensor
Creative Commons
BY 4.0
Artikel in diesem Heft
- Regular articles
- Automated segmentation of thick confocal microscopy 3D images for the measurement of white matter volumes in zebrafish brains
- Image Restoration by Learning Morphological Opening-Closing Network
- Digital Objects in Rhombic Dodecahedron Grid
- Special Issue: ISMM 2019
- Editorial — Special Issue: ISMM 2019
- Hyperspectral Image Classification Based on Mathematical Morphology and Tensor Decomposition
- Boundary Morphology for Hierarchical Simplification of Archaeological Fragments
- Approximating morphological operators with part-based representations learned by asymmetric auto-encoders
- Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions
- Fast marching based superpixels
