2023 - Research.com Computer Science in Germany Leader Award
Juergen Gall mostly deals with Artificial intelligence, Computer vision, Pose, Pattern recognition and 3D pose estimation. His Artificial intelligence research focuses on subjects like Machine learning, which are linked to Image segmentation. His work on Motion capture as part of general Computer vision research is often related to Regression, thus linking different fields of science.
His biological study deals with issues like Hough transform, which deal with fields such as Codebook, Implicit Shape Model, Object and Video tracking. Within one scientific family, Juergen Gall focuses on topics pertaining to Robustness under 3D pose estimation, and may sometimes address concerns connected to Facial recognition system, Facial expression, Graphics, Ground truth and Optical flow estimation. His Object detection research includes themes of Categorization and Contrast.
Juergen Gall mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Pose. His study in Segmentation, Object, Image, Object detection and Convolutional neural network is carried out as part of his Artificial intelligence studies. His Computer vision study deals with Benchmark intersecting with Representation.
His research integrates issues of Supervised learning, Random forest, Hough transform and Inference in his study of Pattern recognition. His biological study spans a wide range of topics, including Visualization, Data mining, Hidden Markov model and Set. As part of the same scientific family, Juergen Gall usually focuses on Pose, concentrating on Training set and intersecting with Field.
The scientist’s investigation covers issues in Artificial intelligence, Segmentation, Machine learning, Pattern recognition and Computer vision. His study in Set extends to Artificial intelligence with its themes. His Segmentation research also works with subjects such as
His studies in Pattern recognition integrate themes in fields like Image quality, Ground truth, Real image and Transformer. His study in Computer vision is interdisciplinary in nature, drawing from both Memorization and Overfitting. His work is dedicated to discovering how Pose, Motion blur are connected with Tracking and other disciplines.
His scientific interests lie mostly in Artificial intelligence, Segmentation, Hidden Markov model, Pattern recognition and Deep learning. He combines subjects such as Data mining and Computer vision with his study of Artificial intelligence. In general Computer vision study, his work on Pose tracking, Pose and Tracking often relates to the realm of Scale, thereby connecting several areas of interest.
His work deals with themes such as Point cloud, Field and Projection, which intersect with Segmentation. His Hidden Markov model research focuses on Supervised learning and how it relates to Transformer and Set. The various areas that Juergen Gall examines in his Pattern recognition study include Ground truth, Real image and Noise.
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.
Hough Forests for Object Detection, Tracking, and Action Recognition
J. Gall;A. Yao;N. Razavi;L. Van Gool.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2011)
Towards Understanding Action Recognition
Hueihan Jhuang;Juergen Gall;Silvia Zuffi;Cordelia Schmid.
international conference on computer vision (2013)
Class-specific Hough forests for object detection
Juergen Gall;Victor Lempitsky.
computer vision and pattern recognition (2009)
Real time head pose estimation with random regression forests
Gabriele Fanelli;Juergen Gall;Luc Van Gool.
computer vision and pattern recognition (2011)
Motion capture using joint skeleton tracking and surface estimation
Juergen Gall;Carsten Stoll;Edilson de Aguiar;Christian Theobalt.
computer vision and pattern recognition (2009)
Real-time facial feature detection using conditional regression forests
Matthias Dantone;Juergen Gall;Gabriele Fanelli;Luc Van Gool.
computer vision and pattern recognition (2012)
Random Forests for Real Time 3D Face Analysis
Gabriele Fanelli;Matthias Dantone;Juergen Gall;Andrea Fossati.
International Journal of Computer Vision (2013)
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Jens Behley;Martin Garbade;Andres Milioto;Jan Quenzel.
international conference on computer vision (2019)
Real time head pose estimation from consumer depth cameras
Gabriele Fanelli;Thibaut Weise;Juergen Gall;Luc Van Gool.
international conference on pattern recognition (2011)
A Hough transform-based voting framework for action recognition
Angela Yao;Juergen Gall;Luc Van Gool.
computer vision and pattern recognition (2010)
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