Wiener-Optimized Discrete Filters for Differential Motion Estimation. - Kai Krajsek; Rudolf Mester (IWCM, 2004)

Signal and Noise Adapted Filters for Differential Motion Estimation. - Kai Krajsek; Rudolf Mester (DAGM-Symposium, 2005)

The Edge Preserving Wiener Filter for Scalar and Tensor Valued Images. - Kai Krajsek; Rudolf Mester (DAGM-Symposium, 2006)

A Maximum Likelihood Estimator for Choosing the Regularization Parameters in Global Optical Flow Methods. - Kai Krajsek; Rudolf Mester (ICIP, 2006)

An unified theory for steerable and quadrature filters. - Kai Krajsek; Rudolf Mester (VISAPP (1), 2006)

A Unified Theory for Steerable and Quadrature Filters. - Kai Krajsek; Rudolf Mester (VISIGRAPP (Selected Papers), 2006)

View-Based Robot Localization Using Spherical Harmonics: Concept and First Experimental Results. - Holger Friedrich 001; David Dederscheck; Kai Krajsek; Rudolf Mester (DAGM-Symposium, 2007)

Bayesian Model Selection for Optical Flow Estimation. - Kai Krajsek; Rudolf Mester (DAGM-Symposium, 2007)

Highly Accurate Orientation Estimation using Steerable Filters. - Jakub K. Kominiarczuk; Kai Krajsek; Rudolf Mester (ICIP (5), 2007)

Statistically Optimal Averaging for Image Restoration and Optical Flow Estimation. - Kai Krajsek; Rudolf Mester; Hanno Scharr (DAGM-Symposium, 2008)

Riemannian Anisotropic Diffusion for Tensor Valued Images. - Kai Krajsek; Marion I. Menzel; Michael Zwanger; Hanno Scharr (ECCV (4), 2008)

Building Blocks for Computer Vision with Stochastic Partial Differential Equations. - Tobias Preusser; Hanno Scharr; Kai Krajsek; Robert M. Kirby (Int. J. Comput. Vis., 2008)

Riemannian Bayesian estimation of diffusion tensor images. - Kai Krajsek; Marion I. Menzel; Hanno Scharr (ICCV, 2009)

Diffusion filtering without parameter tuning: Models and inference tools. - Kai Krajsek; Hanno Scharr (CVPR, 2010)

An Estimation Theoretical Approach to Ambrosio-Tortorelli Image Segmentation. - Kai Krajsek; Ines Dedovic; Hanno Scharr (DAGM-Symposium, 2011)

A Short Introduction to Diffusion-Like Methods. - Hanno Scharr; Kai Krajsek (Mathematical Methods for Signal and Image Analysis and Representation, 2012)

A Riemannian approach for estimating orientation distribution function (ODF) images from high-angular resolution diffusion imaging (HARDI). - Kai Krajsek; Hanno Scharr (CVPR, 2012)

Using Channel Representations in Regularization Terms - A Case Study on Image Diffusion. - Christian Heinemann; Freddie Åström; George Baravdish; Kai Krajsek; Michael Felsberg; Hanno Scharr (VISAPP (1), 2014)

Likelihood Functions for Errors-in-variables Models - Bias-free Local Estimation with Minimum Variance. - Kai Krajsek; Christian Heinemann; Hanno Scharr (VISAPP (3), 2014)

Probabilistic models and estimation: theoretical concepts for variational approaches to computer vision. - Kai Krajsek (2015)

An Online Algorithm for Efficient and Temporally Consistent Subspace Clustering. - Vasileios Zografos; Kai Krajsek; Bjoern H. Menze (ACCV (1), 2016)

A Riemannian Bayesian Framework for Estimating Diffusion Tensor Images. - Kai Krajsek; Marion I. Menzel; Hanno Scharr (Int. J. Comput. Vis., 2016)

HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics. - Markus Götz; Charlotte Debus; Daniel Coquelin; Kai Krajsek; Claudia Comito; Philipp Knechtges; Björn Hagemeier; Michael Tarnawa; Simon Hanselmann; Martin Siggel; Achim Basermann; Achim Streit (IEEE BigData, 2020)

Ensemble Kalman Filter Optimizing Deep Neural Networks: An Alternative Approach to Non-performing Gradient Descent. - Alper Yegenoglu; Kai Krajsek; Sandra Díaz-Pier; Michael Herty (LOD (2), 2020)

JUWELS Booster - A Supercomputer for Large-Scale AI Research. - Stefan Kesselheim; Andreas Herten; Kai Krajsek; Jan Ebert; Jenia Jitsev; Mehdi Cherti; Michael Langguth; Bing Gong; Scarlet Stadtler; Amirpasha Mozaffari; Gabriele Cavallaro; Rocco Sedona; et al. (ISC Workshops, 2021)

Equivariant Representation Learning for Augmentation-based Self-Supervised Learning via Image Reconstruction. - Qin Wang; Kai Krajsek; Hanno Scharr (CoRR, 2024)

Rescuing Easy Samples in Self-Supervised Pretraining. - Qin Wang; Kai Krajsek; Hanno Scharr (VISIGRAPP (2): VISAPP, 2025)

Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks. - Alessio Quercia; Erenus Yildiz; Zhuo Cao; Kai Krajsek; Abigail Morrison; Ira Assent; Hanno Scharr (WACV, 2025)

Self-Supervised Learning based on Transformed Image Reconstruction for Equivariance-Coherent Feature Representation. - Qin Wang; Benjamin Bruns; Hanno Scharr; Kai Krajsek (CoRR, 2025)