H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 74 Citations 68,201 283 World Ranking 613 National Ranking 8

Overview

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.

Top Publications

Multiple view geometry in computer vision

Richard Hartley;Andrew Zisserman.
(2000)

27767 Citations

Bundle Adjustment - A Modern Synthesis

Bill Triggs;Philip F. McLauchlan;Richard I. Hartley;Andrew W. Fitzgibbon.
international conference on computer vision (1999)

4604 Citations

Multiple View Geometry in Computer Vision (2nd ed)

Richard Hartley;Andrew Zisserman.
(2003)

4151 Citations

In defense of the eight-point algorithm

R.I. Hartley.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)

3261 Citations

In defence of the 8-point algorithm

R.I. Hartley.
international conference on computer vision (1995)

2858 Citations

Multiple View Geometry

Richard Hartley;Andrew Zisserman.
(1999)

1744 Citations

Multiple view geometry in computer visiond

Richard Hartley;Andrew Zisserman.
(2001)

1384 Citations

Estimation of Relative Camera Positions for Uncalibrated Cameras

Richard I. Hartley.
european conference on computer vision (1992)

969 Citations

Optimised KD-trees for fast image descriptor matching

C. Silpa-Anan;R. Hartley.
computer vision and pattern recognition (2008)

829 Citations

Subexpression sharing in filters using canonic signed digit multipliers

R.I. Hartley.
IEEE Transactions on Circuits and Systems Ii: Analog and Digital Signal Processing (1996)

743 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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