What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Geometry
His primary areas of study are Artificial intelligence, Computer vision, Pose, 3D pose estimation and Algorithm.
His Artificial intelligence study frequently links to related topics such as Pattern recognition.
His Pattern recognition study incorporates themes from Object, Representation and Articulated body pose estimation.
His Computer vision research includes themes of Deep learning and Representation.
As part of the same scientific family, Kostas Daniilidis usually focuses on 3D pose estimation, concentrating on Optimization problem and intersecting with Set, Quaternion, Cognitive neuroscience of visual object recognition and Metric.
His Algorithm research integrates issues from Parametrization, Vertex and Leverage.
His most cited work include:
- Model-based object tracking in monocular image sequences of road traffic scenes (638 citations)
- Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human Pose (417 citations)
- Catadioptric Projective Geometry (367 citations)
What are the main themes of his work throughout his whole career to date?
Kostas Daniilidis mostly deals with Artificial intelligence, Computer vision, Algorithm, Robot and Pattern recognition.
As part of his studies on Artificial intelligence, Kostas Daniilidis often connects relevant areas like Machine learning.
Kostas Daniilidis merges Computer vision with Event in his research.
His Algorithm study combines topics from a wide range of disciplines, such as Spherical harmonics and Rotation.
His Robot research is multidisciplinary, incorporating elements of Control theory and Human–computer interaction.
His study looks at the relationship between Optical flow and topics such as Artificial neural network, which overlap with Representation.
He most often published in these fields:
- Artificial intelligence (69.23%)
- Computer vision (51.60%)
- Algorithm (14.42%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (69.23%)
- Computer vision (51.60%)
- Algorithm (14.42%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Algorithm, Pose and Machine learning.
His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition.
His work on Optical flow, Motion blur and Rendering as part of general Computer vision research is often related to Event, thus linking different fields of science.
Kostas Daniilidis combines subjects such as Kinematics, Displacement, Inertial frame of reference, Extended Kalman filter and Rotation with his study of Algorithm.
His study in Pose is interdisciplinary in nature, drawing from both Leverage, Identification, Task, Ground truth and Discriminative model.
His Reinforcement learning and Latent variable study, which is part of a larger body of work in Machine learning, is frequently linked to Sample and Prior probability, bridging the gap between disciplines.
Between 2018 and 2021, his most popular works were:
- Learning to Reconstruct 3D Human Pose and Shape via Model-Fitting in the Loop (182 citations)
- Convolutional Mesh Regression for Single-Image Human Shape Reconstruction (164 citations)
- Event-based Vision: A Survey. (162 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Geometry
Kostas Daniilidis spends much of his time researching Artificial intelligence, Pose, Computer vision, Algorithm and Equivariant map.
His Artificial intelligence research is multidisciplinary, relying on both Function and Machine learning.
His studies in Pose integrate themes in fields like Ground truth, Odometry, Leverage and Pattern recognition.
Kostas Daniilidis integrates many fields in his works, including Computer vision and Event.
Kostas Daniilidis interconnects Displacement, Inertial measurement unit, Inertial frame of reference, Attitude and heading reference system and Extended Kalman filter in the investigation of issues within Algorithm.
His research investigates the connection between Equivariant map and topics such as Rotation group SO that intersect with problems in Convolution, Rotation and Generalization.
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