What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of study are Artificial intelligence, Computer vision, Algorithm, Mathematical optimization and Fundamental matrix.
His Artificial intelligence study frequently links to adjacent areas such as Iterative method.
The study incorporates disciplines such as Point and Affine transformation in addition to Computer vision.
As part of the same scientific family, Richard Hartley usually focuses on Fundamental matrix, concentrating on Iterative reconstruction and intersecting with Essential matrix, Computation, Non-linear least squares, Sparse matrix and Reprojection error.
His work deals with themes such as Computer graphics and Bundle adjustment, which intersect with Eight-point algorithm.
His biological study spans a wide range of topics, including Multiple view, Computer graphics and Structure from motion.
His most cited work include:
- Multiple view geometry in computer vision (14643 citations)
- Bundle Adjustment - A Modern Synthesis (2753 citations)
- Multiple View Geometry in Computer Vision (2nd ed) (2746 citations)
What are the main themes of his work throughout his whole career to date?
Richard Hartley spends much of his time researching Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Mathematical optimization.
Image, Pixel, Epipolar geometry, Image processing and Discriminative model are the subjects of his Artificial intelligence studies.
The Computer vision study which covers Geometry that intersects with Computer graphics.
His Algorithm research includes themes of Artificial neural network and Matrix.
Richard Hartley combines subjects such as Function and Convex optimization with his study of Mathematical optimization.
The concepts of his Fundamental matrix study are interwoven with issues in Trifocal tensor and Iterative reconstruction.
He most often published in these fields:
- Artificial intelligence (60.31%)
- Computer vision (41.89%)
- Algorithm (18.42%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (60.31%)
- Computer vision (41.89%)
- Pattern recognition (11.40%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Artificial neural network and Algorithm.
His Discriminative model, Pixel, Image, Object and Deep learning investigations are all subjects of Artificial intelligence research.
He combines Computer vision and Kinematics in his studies.
His Pattern recognition study combines topics from a wide range of disciplines, such as Initialization, Face hallucination and Radial basis function.
Richard Hartley has included themes like Gradient descent, Differentiable function and CUDA in his Algorithm study.
In his study, Motion estimation is inextricably linked to Epipolar geometry, which falls within the broad field of Optical flow.
Between 2016 and 2021, his most popular works were:
- Super-Resolving Very Low-Resolution Face Images with Supplementary Attributes (100 citations)
- Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods (94 citations)
- Face Super-resolution Guided by Facial Component Heatmaps (69 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Richard Hartley mainly investigates Artificial intelligence, Pattern recognition, Computer vision, Artificial neural network and Probabilistic logic.
His Artificial intelligence study focuses mostly on Discriminative model, Deep learning, Pose, Pixel and Face.
The various areas that he examines in his Pattern recognition study include Object, Face hallucination, Set and Radial basis function.
Richard Hartley combines subjects such as Event and Process with his study of Computer vision.
His Artificial neural network research includes elements of Algorithm, Regularization, Training set and Calibration.
His work on Time complexity, Residual and Quantization as part of general Algorithm research is frequently linked to Mean field theory, bridging the gap between disciplines.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
