Helge Ritter focuses on Artificial intelligence, Computer vision, Artificial neural network, Self-organizing map and Robot. The various areas that Helge Ritter examines in his Artificial intelligence study include Machine learning and Pattern recognition. The study incorporates disciplines such as Contextual image classification, Object detection and Kernel regression in addition to Pattern recognition.
His Artificial neural network research incorporates themes from Algorithm, Data mining, Feature and Gesture recognition. His research in Self-organizing map intersects with topics in Dimension, Space, Topology, Robotic arm and Gaussian curvature. In general Robot, his work in Tactile sensor is often linked to Dreyfus model of skill acquisition linking many areas of study.
Helge Ritter mostly deals with Artificial intelligence, Computer vision, Artificial neural network, Robot and Human–computer interaction. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition. His study in Pattern recognition focuses on Feature extraction in particular.
His Computer vision study frequently draws connections between adjacent fields such as GRASP. Humanoid robot, Robot kinematics and Robot control are among the areas of Robot where Helge Ritter concentrates his study. Helge Ritter studies Sonification which is a part of Human–computer interaction.
His main research concerns Artificial intelligence, Computer vision, Robot, Object and Tactile sensor. He specializes in Artificial intelligence, namely Haptic technology. His work in Computer vision addresses issues such as Control theory, which are connected to fields such as Body schema.
Helge Ritter combines subjects such as Simulation and Human–computer interaction with his study of Robot. His Object research is multidisciplinary, incorporating perspectives in Identification, Field and Action. His Tactile sensor research incorporates elements of Image resolution, Motion and Visual servoing.
The scientist’s investigation covers issues in Artificial intelligence, Computer vision, Robot, Tactile sensor and Object. As part of his studies on Artificial intelligence, Helge Ritter frequently links adjacent subjects like Control theory. As a member of one scientific family, Helge Ritter mostly works in the field of Computer vision, focusing on GRASP and, on occasion, Degrees of freedom.
His studies in Robot integrate themes in fields like Pose, Simulation, Human–computer interaction and Identification. His Tactile sensor research integrates issues from Robot end effector, Wearable computer, Embedded system and Action. His Object study deals with Visual servoing intersecting with Visualization, Motion and Image processing.
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.
Neural Computation And Self Organizing Maps: An Introduction
Helge Ritter;Thomas Martinetz;Klaus Schulten;Daniel Barsky.
Self-organizing semantic maps
H. Ritter;T. Kohonen.
Biological Cybernetics (1989)
BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm
M. Kaper;P. Meinicke;U. Grossekathoefer;T. Lingner.
IEEE Transactions on Biomedical Engineering (2004)
Topology-conserving maps for learning visuo-motor-coordination
H. J. Ritter;T. M. Martinetz;K. J. Schulten.
Neural Networks (1989)
Convergence properties of Kohonen's topology conserving maps: fluctuations, stability, and dimension selection
H. Ritter;K. Schulten.
Biological Cybernetics (1988)
On the stationary state of Kohonen's self-organizing sensory mapping
H Ritter;K Schulten.
Biological Cybernetics (1986)
Three-dimensional neural net for learning visuomotor coordination of a robot arm
T.M. Martinetz;H.J. Ritter;K.J. Schulten.
IEEE Transactions on Neural Networks (1990)
A principle for the formation of the spatial structure of cortical feature maps.
Klaus Obermayer;Helge Ritter;Klaus Schulten.
Proceedings of the National Academy of Sciences of the United States of America (1990)
An Adaptive P300-Based Online Brain–Computer Interface
A. Lenhardt;M. Kaper;H.J. Ritter.
international conference of the ieee engineering in medicine and biology society (2008)
Adaptive color segmentation-a comparison of neural and statistical methods
E. Littmann;H. Ritter.
IEEE Transactions on Neural Networks (1997)
Profile was last updated on December 6th, 2021.
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