Stochastic segmentation on images with uncertain data
Abstract: The present work considers a stochastic segmentation method on images in the presence of noise within a PDE‐based image processing framework. Classical methods are not able to capture the error propagation of uncertain estimated input data and their impact on the final segmentation result, which can be of great importance for clinical decisions. Therefore, an intrusive generalized polynomial chaos (gPC) expansion for a stochastic level‐set based geodesic active contours method is proposed. Employing an operator splitting and a stochastic Galerkin projection a deterministic and symmetric non‐linear hyperbolic system can be obtained, which can be treated using common numerical methods.
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
Online-Ressource
- Language
-
Englisch
- Bibliographic citation
-
Stochastic segmentation on images with uncertain data ; volume:21 ; number:1 ; year:2021 ; extent:2
Proceedings in applied mathematics and mechanics ; 21, Heft 1 (2021) (gesamt 2)
- Creator
-
Theilen, Elin
Preusser, Tobias
- DOI
-
10.1002/pamm.202100233
- URN
-
urn:nbn:de:101:1-2021121514095122427237
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:40 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Theilen, Elin
- Preusser, Tobias
Other Objects (12)
Segmentation of Stochastic Images using Stochastic Partial Differential Equations
Modelling uncertain data
Semantic Segmentation of Ambiguous Images
Uncertain data envelopment analysis
Data-driven simulations for training AI-based segmentation of neutron images
CREATING AND USING MASK IMAGES FOR SEGMENTATION IN POINT CLOUD DATA
Multi-scale segmentation of retinal images
Robust Vessel Segmentation in Fundus Images
Volume Segmentation of 3-dimensional Images
SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES
Scan statistics for data segmentation of stochastic processes and anomaly detection in large image data
Enhancing learning on uncertain pixels in self-distillation for object segmentation
Segmentation of Stochastic Images using Stochastic Partial Differential Equations
Modelling uncertain data
Semantic Segmentation of Ambiguous Images
Uncertain data envelopment analysis
Data-driven simulations for training AI-based segmentation of neutron images
CREATING AND USING MASK IMAGES FOR SEGMENTATION IN POINT CLOUD DATA
Multi-scale segmentation of retinal images
Robust Vessel Segmentation in Fundus Images
Volume Segmentation of 3-dimensional Images
SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES
Scan statistics for data segmentation of stochastic processes and anomaly detection in large image data
Enhancing learning on uncertain pixels in self-distillation for object segmentation
Segmentation of Stochastic Images using Stochastic Partial Differential Equations
Modelling uncertain data
Semantic Segmentation of Ambiguous Images
Uncertain data envelopment analysis
Data-driven simulations for training AI-based segmentation of neutron images
CREATING AND USING MASK IMAGES FOR SEGMENTATION IN POINT CLOUD DATA
Multi-scale segmentation of retinal images
Robust Vessel Segmentation in Fundus Images
Volume Segmentation of 3-dimensional Images
SALIENCY BASED SEGMENTATION OF SATELLITE IMAGES
Scan statistics for data segmentation of stochastic processes and anomaly detection in large image data