What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
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 most cited work include:
- A Statistical Model of Human Pose and Body Shape (330 citations)
- Motion capture using joint skeleton tracking and surface estimation (281 citations)
- Automatic human model generation (218 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (79.35%)
- Computer vision (51.39%)
- Pattern recognition (24.18%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (79.35%)
- Computer vision (51.39%)
- Pattern recognition (24.18%)
In recent papers he was focusing on the following fields of study:
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.
Between 2016 and 2021, his most popular works were:
- Recovering Accurate {3D} Human Pose in the Wild Using {IMUs} and a Moving Camera (203 citations)
- Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs (121 citations)
- Fusion of Head and Full-Body Detectors for Multi-object Tracking (97 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
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
- Ground truth which intersects with area such as Object detection, Feature learning, Cross entropy and Photogrammetry,
- Feature extraction which intersects with area such as Feature, Class and Representation.
His Benchmark research incorporates elements of MNIST database, Autoencoder, Data visualization and Visual surveillance.
His research investigates the connection between Pattern recognition and topics such as Vanishing point that intersect with issues in Gaussian surface, Gnomonic projection, Robust statistics and RANSAC.
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