The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Pose, Motion capture and Tracking system. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Skinning and Pattern recognition. His work in Tracking, Motion estimation, Motion, Orientation and Structure from motion is related to Computer vision.
His work deals with themes such as Image processing, Motion analysis and Cluster analysis, which intersect with Pose. Bodo Rosenhahn works mostly in the field of Motion capture, limiting it down to concerns involving Animation and, occasionally, Nonlinear dimensionality reduction, Graphical model and Rendering. His studies examine the connections between Tracking system and genetics, as well as such issues in Object detection, with regards to Scale-invariant feature transform, Optical flow and Linear programming.
His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Pose and Segmentation. His Artificial intelligence study frequently intersects with other fields, such as Machine learning. His study in Tracking, Tracking system, Motion estimation, Silhouette and Motion falls under the purview of Computer vision.
His Pattern recognition research integrates issues from Object detection, Image and Random forest. His 3D pose estimation and Articulated body pose estimation study in the realm of Pose interacts with subjects such as Conformal geometric algebra. His Segmentation study frequently draws connections between adjacent fields such as Subspace topology.
Bodo Rosenhahn focuses on Artificial intelligence, Computer vision, Pattern recognition, Artificial neural network and Machine learning. His Artificial intelligence study focuses mostly on Convolutional neural network, Benchmark, Feature, Segmentation and Object detection. His study in Computer vision concentrates on Tracking, Tracking system, Pose, Monocular and Motion capture.
His Pose research includes elements of 2D to 3D conversion, Inertial measurement unit and Overfitting. His Pattern recognition research is multidisciplinary, incorporating elements of Vanishing point, Deep learning, Robustness and Contrast. His Artificial neural network research includes elements of Robust statistics and RANSAC.
His primary scientific interests are in Artificial intelligence, Computer vision, Convolutional neural network, Benchmark and Pattern recognition. The Artificial intelligence study combines topics in areas such as Machine learning and Detector. His studies in Tracking system, Tracking, Orientation, Inertial measurement unit and 3D pose estimation are all subfields of Computer vision research.
His study on Convolutional neural network also encompasses disciplines like
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.
Motion capture using joint skeleton tracking and surface estimation
Juergen Gall;Carsten Stoll;Edilson de Aguiar;Christian Theobalt.
computer vision and pattern recognition (2009)
A Statistical Model of Human Pose and Body Shape
Nils Hasler;Carsten Stoll;Martin Sunkel;Bodo Rosenhahn.
Computer Graphics Forum (2009)
Automatic human model generation
Bodo Rosenhahn;Lei He;Reinhard Klette.
computer analysis of images and patterns (2005)
Optimization and Filtering for Human Motion Capture
Juergen Gall;Bodo Rosenhahn;Thomas Brox;Hans-Peter Seidel.
International Journal of Computer Vision (2010)
Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker
Laura Leal-Taixe;Gerard Pons-Moll;Bodo Rosenhahn.
international conference on computer vision (2011)
Recovering Accurate {3D} Human Pose in the Wild Using {IMUs} and a Moving Camera
Timo von Marcard;Roberto Henschel;Michael J. Black;Bodo Rosenhahn.
european conference on computer vision (2018)
Learning an Image-Based Motion Context for Multiple People Tracking
Laura Leal-Taixé;Michele Fenzi;Alina Kuznetsova;Bodo Rosenhahn.
computer vision and pattern recognition (2014)
Complementary Optic Flow
Henning Zimmer;Andrés Bruhn;Joachim Weickert;Levi Valgaerts.
energy minimization methods in computer vision and pattern recognition (2009)
Markerless Motion Capture with unsynchronized moving cameras
Nils Hasler;Bodo Rosenhahn;Thorsten Thormahlen;Michael Wand.
computer vision and pattern recognition (2009)
Three-Dimensional Shape Knowledge for Joint Image Segmentation and Pose Tracking
Bodo Rosenhahn;Thomas Brox;Joachim Weickert.
International Journal of Computer Vision (2007)
Profile was last updated on December 6th, 2021.
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