 
					Dr. Aditya Tewari
| E-Mail: | aditya.tewari@dfki.de | 
|---|---|
| Position: | External Member | 
Aditya’s primary interests include feature learning, time series analysis, and trend detection in data. He is Presently working on Machine Learning Algorithms that can be utilised for motion learning. The present focus of the research is on neural network architectures for hand-pose and hand gesture recognition while using low accuracy, low-resolution time of flight camera.
Aditya completed his Masters in Systems and Signal Processing at the University of Southampton, UK at CSPC group which is now the Vision, Learning and Control Group. He completed his Masters thesis on locating curvilinear features in Noisy Bronchial Wall Confocal Images in 2013. Earlier, he completed his Bachelors from the G.B.Pant university Pantnagar in 2010.
								A Probablistic Combination of CNN and RNN Estimates for Hand Gesture Based Interaction in Car
								
								16th IEEE International Symposium on Mixed and Augmented  Reality (ISMAR) IEEE International Symposium on Mixed and Augmented Reality (ISMAR-17), October 9-13, Nantes, France								
								
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								Adding Model Constraints to CNN for Top View Hand Pose Recognition in Range Images
								
								Proceedings of the 5th International Conference  in Pattern Recognition Applications and Methods ICPRAM 2016 International Conference on Pattern Recognition Applications and Methods (ICPRAM-05), 5th, February 24-26, Rome, Italy								
								
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								Learning to Fuse: A Deep Learning Approach to Visual-Inertial Camera Pose Estimation
								
								 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-2016), September 19-23, Merida, Mexico								
								
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								Two Phase Classification for Early Hand Gesture Recognition in 3D Top View Data
								
								International Symposium on Visual Computing : Advances in Visual Computing International Conference on Visual Computing (ISVC-16), Advances in Visual Computing, December 12-14, Las Vegas, Nevada, USA								
								
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								Using Mutual Independence of Slow Features for Improved Information Extraction and Better Hand-Pose Classification
								
																
								
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