What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Programming language
His primary areas of investigation include Artificial intelligence, Computer vision, Robot, Segmentation and Pattern recognition.
His Robotics, Eye tracking, Gesture, Tracking system and Object investigations are all subjects of Artificial intelligence research.
In his study, which falls under the umbrella issue of Computer vision, Motion estimation and Robust statistics is strongly linked to Robustness.
His research in Robot intersects with topics in Control engineering, Simulation and Trajectory.
Gregory D. Hager has included themes like Manipulator and Motion control in his Control engineering study.
His Segmentation study incorporates themes from Classifier and Recurrent neural network.
His most cited work include:
- A tutorial on visual servo control (3039 citations)
- Efficient region tracking with parametric models of geometry and illumination (1058 citations)
- Advances in computational stereo (1047 citations)
What are the main themes of his work throughout his whole career to date?
Gregory D. Hager focuses on Artificial intelligence, Computer vision, Robot, Pattern recognition and Segmentation.
As part of his studies on Artificial intelligence, Gregory D. Hager often connects relevant subjects like Machine learning.
His Computer vision study integrates concerns from other disciplines, such as Imaging phantom, Robustness and Ultrasound.
Specifically, his work in Ultrasound is concerned with the study of Elastography.
His Robot research integrates issues from Control engineering, Motion, Simulation and Human–computer interaction.
His study in Segmentation focuses on Image segmentation in particular.
He most often published in these fields:
- Artificial intelligence (72.19%)
- Computer vision (44.53%)
- Robot (18.97%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (72.19%)
- Computer vision (44.53%)
- Robot (18.97%)
In recent papers he was focusing on the following fields of study:
Gregory D. Hager spends much of his time researching Artificial intelligence, Computer vision, Robot, Pattern recognition and Machine learning.
In most of his Artificial intelligence studies, his work intersects topics such as Domain.
His Robot research incorporates themes from Motion, Human–computer interaction and Reinforcement learning.
His work on Multi-label classification as part of general Pattern recognition study is frequently linked to Generality, bridging the gap between disciplines.
The Machine learning study combines topics in areas such as Key, State, Task and Code.
The study incorporates disciplines such as Segmentation, Scene graph, Annotation, Pixel and Image in addition to Object.
Between 2018 and 2021, his most popular works were:
- Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions: a systematic review (34 citations)
- Semantic Stereo for Incidental Satellite Images (34 citations)
- Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques. (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Programming language
His main research concerns Artificial intelligence, Deep learning, Machine learning, Convolutional neural network and Robot.
His studies deal with areas such as Code, Computer vision and Pattern recognition as well as Artificial intelligence.
His Computer vision research is multidisciplinary, incorporating perspectives in Graphical model, Granularity and Pairwise comparison.
His work on Activity recognition is typically connected to Variance as part of general Machine learning study, connecting several disciplines of science.
His study looks at the relationship between Convolutional neural network and fields such as Ground truth, as well as how they intersect with chemical problems.
In his work, Mobile robot, Human–computer interaction and Contrast is strongly intertwined with Motion, which is a subfield of Robot.
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