A sensor independent probabilistic fusion system for driver assistance systems. - Michael Munz 001; Klaus C. J. Dietmayer; Mirko Mählisch (ITSC, 2009)

Generalized fusion of heterogeneous sensor measurements for multi target tracking. - Michael Munz 001; Klaus Dietmayer; Mirko Mählisch (FUSION, 2010)

Probabilistic modeling of sensor properties in generic fusion systems for modern driver assistance systems. - Michael Munz 001; Mirko Mählisch; Jürgen Dickmann; Klaus Dietmayer (Intelligent Vehicles Symposium, 2010)

Generic Centralized Multi Sensor Data Fusion Based on Probabilistic Sensor and Environment Models for Driver Assistance Systems. - Michael Munz 001; Mirko Mählisch; Klaus Dietmayer (IEEE Intell. Transp. Syst. Mag., 2010)

Using Dempster-Shafer-based modeling of object existence evidence in sensor fusion systems for advanced driver assistance systems. - Michael Munz 001; Klaus Dietmayer (Intelligent Vehicles Symposium, 2011)

Using grid maps to reduce the number of false positive measurements in advanced driver assistance systems. - Dominik Nuss; Stephan Reuter; Marcus Konrad; Michael Munz 001; Klaus Dietmayer (ITSC, 2012)

Impact of out-of-sequence measurements on the joint integrated probabilistic data association filter for vehicle safety systems. - Antje Westenberger; Bharanidhar Duraisamy; Michael Munz 001; Marc M. Muntzinger; Martin Fritzsche; Klaus Dietmayer (Intelligent Vehicles Symposium, 2012)

Real-Time Multi-Object Tracking using Random Finite Sets. - Stephan Reuter; Benjamin Wilking; Jürgen Wiest; Michael Munz 001; Klaus Dietmayer (IEEE Trans. Aerosp. Electron. Syst., 2013)

Analysis of Gait-Event-related Brain Potentials During Instructed And Spontaneous Treadmill Walking - Technical Affordances and used Methods. - Cornelia Herbert; Jan Nachtsheim; Michael Munz 001 (CoRR, 2020)

Novel Approach To Synchronisation Of Wearable IMUs Based On Magnetometers. - Andreas Spilz; Michael Munz 001 (CoRR, 2021)

Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition. - David Kreuzer; Michael Munz 001 (Sensors, 2021)

Transformer-Based UNet with Multi-Headed Cross-Attention Skip Connections to Eliminate Artifacts in Scanned Documents. - David Kreuzer; Michael Munz 001 (CoRR, 2023)

Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures. - Andreas Spilz; Michael Munz 001 (Sensors, 2023)

Towards Explainable Visual Vessel Recognition Using Fine-Grained Classification and Image Retrieval. - Heiko Karus; Friedhelm Schwenker; Michael Munz 001; Michael Teutsch (CVPR Workshops, 2024)

IMUDiffusion: A Diffusion Model for Multivariate Time Series Synthetisation for Inertial Motion Capturing Systems. - Heiko Oppel; Michael Munz 001 (CoRR, 2024)

Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs. - Heiko Oppel; Andreas Spilz; Michael Munz 001 (CoRR, 2025)

Boosting Automatic Exercise Evaluation Through Musculoskeletal Simulation-Based IMU Data Augmentation. - Andreas Spilz; Heiko Oppel; Michael Munz 001 (CoRR, 2025)

GAITEX: Human motion dataset from impaired gait and rehabilitation exercises of inertial and optical sensor data. - Andreas Spilz; Heiko Oppel; Jochen Werner 003; Kathrin Stucke-Straub; Felix Capanni; Michael Munz 001 (CoRR, 2025)