H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 37 Citations 12,902 57 World Ranking 5327 National Ranking 2616

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

Bryan C. Russell mainly investigates Artificial intelligence, Pattern recognition, Object, Image segmentation and Cognitive neuroscience of visual object recognition. Bryan C. Russell combines subjects such as Computer vision and Natural language processing with his study of Artificial intelligence. His specific area of interest is Pattern recognition, where Bryan C. Russell studies Segmentation.

His study looks at the relationship between Object and topics such as Probabilistic latent semantic analysis, which overlap with Contextual image classification. His Cognitive neuroscience of visual object recognition research integrates issues from Object detection and LabelMe. The study incorporates disciplines such as Data mining, WordNet, Information retrieval, Automatic image annotation and Supervised learning in addition to Object detection.

His most cited work include:

  • LabelMe: A Database and Web-Based Tool for Image Annotation (2315 citations)
  • Discovering objects and their location in images (944 citations)
  • Using Multiple Segmentations to Discover Objects and their Extent in Image Collections (608 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Object and Image. In his study, Face and Projection is strongly linked to Polygon mesh, which falls under the umbrella field of Artificial intelligence. His work on Pixel, Object detection and 3D single-object recognition as part of general Computer vision research is frequently linked to Process, thereby connecting diverse disciplines of science.

His research investigates the connection between Pattern recognition and topics such as Margin that intersect with problems in Shape matching, Base, Pooling and Training set. His Object study integrates concerns from other disciplines, such as Artificial neural network, LabelMe, Database and Image retrieval. His study looks at the relationship between Image and fields such as Surface, as well as how they intersect with chemical problems.

He most often published in these fields:

  • Artificial intelligence (83.33%)
  • Computer vision (42.86%)
  • Pattern recognition (32.14%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (83.33%)
  • Computer vision (42.86%)
  • RGB color model (10.71%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial intelligence, Computer vision, RGB color model, Object and Pattern recognition. His study on Artificial intelligence is mostly dedicated to connecting different topics, such as Information retrieval. In general Computer vision study, his work on Image, Background image and Vanishing point often relates to the realm of Process, thereby connecting several areas of interest.

His Image research includes themes of Surface and Task. His Object research is multidisciplinary, incorporating perspectives in Artificial neural network and Field. His work in the fields of Pattern recognition, such as Segmentation, intersects with other areas such as Deformation.

Between 2018 and 2021, his most popular works were:

  • FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape From Single RGB Images (93 citations)
  • Learning elementary structures for 3D shape generation and matching (67 citations)
  • Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction (33 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His primary areas of investigation include Artificial intelligence, Pattern recognition, RGB color model, Deformation and Computer vision. Artificial intelligence is often connected to Speech recognition in his work. His Pattern recognition research is multidisciplinary, incorporating elements of Object, Generalization, Benchmark and Sample.

His RGB color model research includes elements of 3D reconstruction, Polygon mesh, Face and Piecewise. Shape matching, Margin, Training set, SIGNAL and Segmentation are fields of study that intersect with his Deformation study. When carried out as part of a general Computer vision research project, his work on Image is frequently linked to work in Collision, therefore connecting diverse disciplines of study.

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

LabelMe: A Database and Web-Based Tool for Image Annotation

Bryan C. Russell;Antonio Torralba;Kevin P. Murphy;William T. Freeman.
International Journal of Computer Vision (2008)

2988 Citations

Discovering objects and their location in images

J. Sivic;B.C. Russell;A.A. Efros;A. Zisserman.
international conference on computer vision (2005)

1623 Citations

Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

B.C. Russell;W.T. Freeman;A.A. Efros;J. Sivic.
computer vision and pattern recognition (2006)

816 Citations

Discovering object categories in image collections

Josef Sivic;Bryan C. Russell;Alexei A. Efros;Andrew Zisserman.
(2005)

626 Citations

A Papier-Mâché Approach to Learning 3D Surface Generation

Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell.
computer vision and pattern recognition (2018)

464 Citations

Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD Models

Mathieu Aubry;Daniel Maturana;Alexei A. Efros;Alexei A. Efros;Bryan C. Russell.
computer vision and pattern recognition (2014)

414 Citations

ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification

Rohit Girdhar;Rohit Girdhar;Deva Ramanan;Abhinav Gupta;Josef Sivic.
computer vision and pattern recognition (2017)

335 Citations

AtlasNet: A Papier-Mâché Approach to Learning 3D Surface Generation

Thibault Groueix;Matthew Fisher;Vladimir G. Kim;Bryan C. Russell.
computer vision and pattern recognition (2018)

313 Citations

Unsupervised discovery of visual object class hierarchies

J. Sivic;B.C. Russell;A. Zisserman;W.T. Freeman.
computer vision and pattern recognition (2008)

285 Citations

Dataset issues in object recognition

Jean Ponce;Jean Ponce;Tamara L. Berg;Mark Everingham;David A. Forsyth.
Lecture Notes in Computer Science (2006)

255 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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Top Scientists Citing Bryan C. Russell

Antonio Torralba

Antonio Torralba

MIT

Publications: 78

Andrew Zisserman

Andrew Zisserman

University of Oxford

Publications: 54

Leonidas J. Guibas

Leonidas J. Guibas

Stanford University

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Alexei A. Efros

Alexei A. Efros

University of California, Berkeley

Publications: 49

Li Fei-Fei

Li Fei-Fei

Stanford University

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Kristen Grauman

Kristen Grauman

Facebook (United States)

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Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 40

Josef Sivic

Josef Sivic

Czech Technical University in Prague

Publications: 38

William T. Freeman

William T. Freeman

MIT

Publications: 38

Abhinav Gupta

Abhinav Gupta

Facebook (United States)

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Martial Hebert

Martial Hebert

Carnegie Mellon University

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Hao Su

Hao Su

University of California, San Diego

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Vittorio Ferrari

Vittorio Ferrari

Google (United States)

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Derek Hoiem

Derek Hoiem

University of Illinois at Urbana-Champaign

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Jitendra Malik

Jitendra Malik

University of California, Berkeley

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