H-Index & Metrics Best Publications
William T. Freeman

William T. Freeman

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 120 Citations 82,219 316 World Ranking 51 National Ranking 34

Research.com Recognitions

Awards & Achievements

2020 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to computer vision and machine learning, especially the development of steerable filters, belief propagation, and motion re-rendering.

2016 - ACM Fellow For contributions to low-level computer vision, machine learning, and computer graphics.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

William T. Freeman mainly focuses on Artificial intelligence, Computer vision, Pattern recognition, Object and Algorithm. As a part of the same scientific family, he mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Inference. His work investigates the relationship between Computer vision and topics such as Representation that intersect with problems in Histogram.

The study incorporates disciplines such as Probabilistic latent semantic analysis and Prior probability in addition to Pattern recognition. His Cognitive neuroscience of visual object recognition study in the realm of Object connects with subjects such as Bag-of-words model in computer vision. William T. Freeman has researched Algorithm in several fields, including Graph theory, Combinatorics and Color constancy.

His most cited work include:

  • The design and use of steerable filters (2682 citations)
  • LabelMe: A Database and Web-Based Tool for Image Annotation (2315 citations)
  • Example-based super-resolution (2052 citations)

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

William T. Freeman mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Image and Object. His Artificial intelligence research incorporates elements of Machine learning and Computer graphics. His work in Iterative reconstruction, Rendering, Motion estimation, Optical flow and Ground truth are all subfields of Computer vision research.

His Pattern recognition research is multidisciplinary, incorporating perspectives in Object detection and Inference. Image connects with themes related to Algorithm in his study. His biological study focuses on Cognitive neuroscience of visual object recognition.

He most often published in these fields:

  • Artificial intelligence (78.56%)
  • Computer vision (58.69%)
  • Pattern recognition (19.64%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (78.56%)
  • Computer vision (58.69%)
  • Pattern recognition (19.64%)

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

Artificial intelligence, Computer vision, Pattern recognition, Object and Image are his primary areas of study. His work on Artificial intelligence deals in particular with Rendering, Motion, Inpainting, Artificial neural network and Convolutional neural network. He interconnects Ground truth, Differentiable function, Real image and Image retrieval in the investigation of issues within Rendering.

As part of his studies on Computer vision, he often connects relevant subjects like Code. His Pattern recognition research includes themes of 3D reconstruction, Solid modeling, Matching, Structure and Pose. His Object research also works with subjects such as

  • Representation that intertwine with fields like Graphics,
  • Human–computer interaction and related Stability.

Between 2017 and 2021, his most popular works were:

  • First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole (1011 citations)
  • First M87 Event Horizon Telescope Results. I. The Shadow of the Supermassive Black Hole (1011 citations)
  • First M87 Event Horizon Telescope Results. IV. Imaging the Central Supermassive Black Hole (368 citations)

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

  • Artificial intelligence
  • Computer vision
  • Statistics

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Computer vision, Object and Deep learning. His study in Iterative reconstruction, Rendering, Artificial neural network, Motion and Silhouette falls within the category of Artificial intelligence. His work deals with themes such as Structure, 3D reconstruction, Pose and Solid modeling, which intersect with Pattern recognition.

William T. Freeman conducts interdisciplinary study in the fields of Computer vision and Video denoising through his research. His Object research is multidisciplinary, incorporating elements of Stability, Adversarial system and Human–computer interaction. His studies examine the connections between Deep learning and genetics, as well as such issues in Speech recognition, with regards to Sound recording and reproduction, Natural, Ambient noise level and Unsupervised learning.

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.

Best Publications

The design and use of steerable filters

W.T. Freeman;E.H. Adelson.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1991)

4173 Citations

Example-based super-resolution

W.T. Freeman;T.R. Jones;E.C. Pasztor.
IEEE Computer Graphics and Applications (2002)

3018 Citations

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

Image quilting for texture synthesis and transfer

Alexei A. Efros;William T. Freeman.
international conference on computer graphics and interactive techniques (2001)

2864 Citations

Removing camera shake from a single photograph

Rob Fergus;Barun Singh;Aaron Hertzmann;Sam T. Roweis.
international conference on computer graphics and interactive techniques (2006)

2088 Citations

Learning Low-Level Vision

William T. Freeman;Egon C. Pasztor;Owen T. Carmichael.
International Journal of Computer Vision (2000)

2041 Citations

Shiftable multiscale transforms

E.P. Simoncelli;W.T. Freeman;E.H. Adelson;D.J. Heeger.
IEEE Transactions on Information Theory (1992)

1877 Citations

80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition

A. Torralba;R. Fergus;W.T. Freeman.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)

1782 Citations

Understanding belief propagation and its generalizations

Jonathan S. Yedidia;William T. Freeman;Yair Weiss.
Exploring artificial intelligence in the new millennium (2003)

1777 Citations

Constructing free-energy approximations and generalized belief propagation algorithms

J.S. Yedidia;W.T. Freeman;Y. Weiss.
IEEE Transactions on Information Theory (2005)

1666 Citations

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