Ehrhardt, I.[Ina]
* 2019: Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers
Ehrhardt, J.[Jan]
* 2007: Variational Approach for Combined Segmentation and Estimation of Respiratory Motion in Temporal Image Sequences, A
* 2011: Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge
* 2011: Statistical Modeling of 4D Respiratory Lung Motion Using Diffeomorphic Image Registration
* 2012: Fast Explicit Diffusion for Registration with Direction-Dependent Regularization
* 2015: Probabilistic Appearance Models for Segmentation and Classification
* 2016: Patch-Based Low-Rank Matrix Completion for Learning of Shape and Motion Models from Few Training Samples
* 2020: Registration with probabilistic correspondences: Accurate and robust registration for pathological and inhomogeneous medical data
Includes: Ehrhardt, J.[Jan] Ehrhardt, J.
7 for Ehrhardt, J.
Ehrhardt, M.J.
* 2014: Vector-Valued Image Processing by Parallel Level Sets
* 2016: Multicontrast MRI Reconstruction with Structure-Guided Total Variation
* 2016: PET Reconstruction With an Anatomical MRI Prior Using Parallel Level Sets
* 2018: Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning
* 2020: Learning the Sampling Pattern for MRI
* 2021: An overview of) Synergistic reconstruction for multimodality/multichannel imaging methods
* 2021: Choose Your Path Wisely: Gradient Descent in a Bregman Distance Framework
* 2021: Convergence Properties of a Randomized Primal-dual Algorithm with Applications to Parallel MRI
* 2021: Motion estimation and correction for simultaneous PET/MR using SIRF and CIL
* 2021: Synergistic Multi-Spectral CT Reconstruction with Directional Total Variation
* 2023: Imaging With Equivariant Deep Learning: From unrolled network design to fully unsupervised learning
Includes: Ehrhardt, M.J. Ehrhardt, M.J.[Matthias J.]
11 for Ehrhardt, M.J.
Ehrhardt, S.[Sebastien]
* 2018: Unsupervised Intuitive Physics from Visual Observations
* 2019: Learning to Discover Novel Visual Categories via Deep Transfer Clustering
* 2019: Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning
* 2019: Taking visual motion prediction to new heightfields
* 2020: Semi-Supervised Learning with Scarce Annotations
* 2021: Co-Attention for Conditioned Image Matching
* 2021: LSD-C: Linearly Separable Deep Clusters
* 2022: AutoNovel: Automatically Discovering and Learning Novel Visual Categories
Includes: Ehrhardt, S.[Sebastien] Ehrhardt, S.[Sébastien] Ehrhardt, S.
8 for Ehrhardt, S.
Ehrhold, A.
* 2012: Keypoint-Based Analysis of Sonar Images: Application to Seabed Recognition