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.
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.
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.
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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A tutorial on visual servo control
S. Hutchinson;G.D. Hager;P.I. Corke.
international conference on robotics and automation (1996)
Advances in computational stereo
M.Z. Brown;D. Burschka;G.D. Hager.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)
Efficient region tracking with parametric models of geometry and illumination
G.D. Hager;P.N. Belhumeur.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)
Fast and globally convergent pose estimation from video images
C.-P. Lu;G.D. Hager;E. Mjolsness.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)
Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions
Rizwan Chaudhry;Avinash Ravichandran;Gregory Hager;Rene Vidal.
computer vision and pattern recognition (2009)
Adaptive and generic corner detection based on the accelerated segment test
Elmar Mair;Gregory D. Hager;Darius Burschka;Michael Suppa.
european conference on computer vision (2010)
Probabilistic data association methods for tracking complex visual objects
C. Rasmussen;G.D. Hager.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)
Gregory D. Hager;Kentaro Toyama.
Computer Vision and Image Understanding (1998)
Vision-assisted control for manipulation using virtual fixtures
A. Bettini;P. Marayong;S. Lang;A.M. Okamura.
IEEE Transactions on Robotics (2004)
Multiple kernel tracking with SSD
G.D. Hager;M. Dewan;C.V. Stewart.
computer vision and pattern recognition (2004)
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