Didier Stricker mainly investigates Artificial intelligence, Computer vision, Augmented reality, Sensor fusion and Multimedia. As part of one scientific family, Didier Stricker deals mainly with the area of Computer vision, narrowing it down to issues related to the Trajectory, and often Minimum bounding box. His work carried out in the field of Augmented reality brings together such families of science as Image processing, Mobile computing and Rendering.
Within one scientific family, Didier Stricker focuses on topics pertaining to Inertial measurement unit under Sensor fusion, and may sometimes address concerns connected to Match moving, Inertial frame of reference, Kinematics and Motion. His studies deal with areas such as Animation, Information and Communications Technology, The Internet, Cultural heritage and Cyber-physical system as well as Multimedia. His research in Computer-mediated reality focuses on subjects like Data visualization, which are connected to Computer graphics.
Didier Stricker spends much of his time researching Artificial intelligence, Computer vision, Augmented reality, Pattern recognition and Pose. His Artificial intelligence study focuses mostly on Deep learning, Convolutional neural network, Robustness, Artificial neural network and Segmentation. His Computer vision study frequently draws parallels with other fields, such as Computer graphics.
His research in Augmented reality intersects with topics in Visualization, Multimedia and Rendering. Didier Stricker studies 3D pose estimation which is a part of Pose. Didier Stricker has included themes like Inertial measurement unit and Accelerometer in his Sensor fusion study.
Artificial intelligence, Computer vision, Deep learning, Artificial neural network and RGB color model are his primary areas of study. Artificial intelligence is often connected to Pattern recognition in his work. His research integrates issues of Lidar and Interpolation in his study of Computer vision.
His Artificial neural network research is multidisciplinary, incorporating perspectives in Point cloud, Image segmentation, Object detection and Data set. His RGB color model research is multidisciplinary, incorporating elements of Matching and Process. His Augmented reality study integrates concerns from other disciplines, such as Tracking system, Tracking, Computation and Feature.
Didier Stricker focuses on Artificial intelligence, Computer vision, Interpolation, Robustness and RGB color model. His studies link Pattern recognition with Artificial intelligence. His Computer vision research incorporates elements of Semantics and Virtual reality.
His Interpolation research includes elements of Optical flow, Algorithm and Pixel. His work deals with themes such as Segmentation and Process, which intersect with RGB color model. His study looks at the relationship between Deep learning and topics such as Pose, which overlap with Convolutional neural network, Augmented reality, Tracking system, Real image and Image resolution.
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Introducing a New Benchmarked Dataset for Activity Monitoring
Attila Reiss;Didier Stricker.
international symposium on wearable computers (2012)
Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet
Jorge Posada;Carlos Toro;Inigo Barandiaran;David Oyarzun.
IEEE Computer Graphics and Applications (2015)
Archeoguide: an augmented reality guide for archaeological sites
V. Vlahakis;M. Ioannidis;J. Karigiannis;M. Tsotros.
IEEE Computer Graphics and Applications (2002)
Archeoguide: first results of an augmented reality, mobile computing system in cultural heritage sites
Vassilios Vlahakis;John Karigiannis;Manolis Tsotros;Michael Gounaris.
visual analytics science and technology (2001)
Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion
Alessandro Filippeschi;Norbert Schmitz;Markus Miezal;Gabriele Bleser;Gabriele Bleser.
Augmented reality for construction tasks: doorlock assembly
Dirk Reiners;Didier Stricker;Gudrun Klinker;Stefan Müller.
IWAR '98 Proceedings of the international workshop on Augmented reality : placing artificial objects in real scenes: placing artificial objects in real scenes (1999)
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
Christian Bailer;Bertram Taetz;Didier Stricker.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)
Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision
Oliver Wasenmüller;Didier Stricker.
asian conference on computer vision (2016)
Creating and benchmarking a new dataset for physical activity monitoring
Attila Reiss;Didier Stricker.
pervasive technologies related to assistive environments (2012)
Advanced tracking through efficient image processing and visual-inertial sensor fusion
G. Bleser;D. Stricker.
ieee virtual reality conference (2008)
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