What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Operating system
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.
His most cited work include:
- Introducing a New Benchmarked Dataset for Activity Monitoring (415 citations)
- Visual Computing as a Key Enabling Technology for Industrie 4.0 and Industrial Internet (310 citations)
- Archeoguide: an augmented reality guide for archaeological sites (270 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (77.78%)
- Computer vision (55.23%)
- Augmented reality (21.24%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (77.78%)
- Computer vision (55.23%)
- Deep learning (6.54%)
In recent papers he was focusing on the following fields of 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.
Between 2019 and 2021, his most popular works were:
- Amharic OCR : An End-to-End Learning (4 citations)
- DeepLiDARFlow: A Deep Learning Architecture For Scene Flow Estimation Using Monocular Camera and Sparse LiDAR (3 citations)
- AutoPOSE: Large-scale Automotive Driver Head Pose and Gaze Dataset with Deep Head Orientation Baseline. (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Operating system
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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