2023 - Research.com Computer Science in Germany Leader Award
Stefan Roth mainly investigates Artificial intelligence, Optical flow, Computer vision, Robustness and Pattern recognition. In his study, which falls under the umbrella issue of Artificial intelligence, Visual odometry is strongly linked to Machine learning. His study in Optical flow is interdisciplinary in nature, drawing from both Algorithm and Noise.
His work on Motion estimation and Segmentation as part of general Computer vision research is often related to Random field, thus linking different fields of science. The Robustness study combines topics in areas such as Mathematical optimization and Hidden Markov model. In general Pattern recognition study, his work on Conditional random field often relates to the realm of Stochastic process, thereby connecting several areas of interest.
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Optical flow and Metallurgy. In Artificial intelligence, Stefan Roth works on issues like Machine learning, which are connected to Motion. His work in Computer vision addresses issues such as Benchmark, which are connected to fields such as Data mining and Tracking.
His Pattern recognition research integrates issues from Image and Feature. His Optical flow research includes elements of Pixel, Algorithm and Probabilistic logic. Stefan Roth has included themes like Amorphous solid, Curie temperature and Analytical chemistry in his Metallurgy study.
Artificial intelligence, Pattern recognition, Deep learning, Image and Algorithm are his primary areas of study. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Computer vision. Stefan Roth has researched Machine learning in several fields, including Motion and Tracking.
His biological study spans a wide range of topics, including Matching, Object detection, Pooling and Feature. His Image study also includes
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Algorithm, Pattern recognition and Artificial neural network. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Computer vision. His research integrates issues of Flow estimation, Inverse problem and Transformer in his study of Computer vision.
His Machine learning study incorporates themes from Tracking, Benchmark and Pattern recognition. His studies deal with areas such as Pixel, Voxel and Similarity as well as Algorithm. Stefan Roth interconnects Probabilistic logic and Robustness in the investigation of issues within Artificial neural network.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts;Mohamed Omran;Sebastian Ramos;Timo Rehfeld.
computer vision and pattern recognition (2016)
A Database and Evaluation Methodology for Optical Flow
Simon Baker;Daniel Scharstein;J. P. Lewis;Stefan Roth.
International Journal of Computer Vision (2011)
Secrets of optical flow estimation and their principles
Deqing Sun;Stefan Roth;Michael J. Black.
computer vision and pattern recognition (2010)
Fields of Experts: a framework for learning image priors
S. Roth;M.J. Black.
computer vision and pattern recognition (2005)
People-tracking-by-detection and people-detection-by-tracking
M. Andriluka;S. Roth;B. Schiele.
computer vision and pattern recognition (2008)
MOT16: A Benchmark for Multi-Object Tracking
Anton Milan;Laura Leal-Taixé;Ian D. Reid;Stefan Roth.
arXiv: Computer Vision and Pattern Recognition (2016)
Pictorial structures revisited: People detection and articulated pose estimation
Mykhaylo Andriluka;Stefan Roth;Bernt Schiele.
computer vision and pattern recognition (2009)
Playing for Data: Ground Truth from Computer Games
Stephan R. Richter;Vibhav Vineet;Stefan Roth;Vladlen Koltun.
european conference on computer vision (2016)
Fields of Experts
Stefan Roth;Michael J. Black.
International Journal of Computer Vision (2009)
MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking
Laura Leal-Taixé;Anton Milan;Ian D. Reid;Stefan Roth.
arXiv: Computer Vision and Pattern Recognition (2015)
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