H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 69 Citations 29,412 280 World Ranking 880 National Ranking 14

Research.com Recognitions

Awards & Achievements

2019 - SIAM Fellow For contributions to shape reconstruction, image processing, and geometric analysis.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Geometry

Ron Kimmel focuses on Artificial intelligence, Computer vision, Algorithm, Image processing and Geodesic. His Artificial intelligence study integrates concerns from other disciplines, such as Metric and Pattern recognition. Ron Kimmel has included themes like Multidimensional scaling, Facial recognition system, Mathematical morphology and Shape analysis in his Algorithm study.

His work deals with themes such as Grid, Numerical analysis, Fast marching method and Embedding, which intersect with Geodesic. His research investigates the connection between Edge detection and topics such as Active contour model that intersect with problems in Topology and Laplace operator. Ron Kimmel combines subjects such as Object detection and Minimal surface with his study of Image segmentation.

His most cited work include:

  • Geodesic active contours (5121 citations)
  • Computing geodesic paths on manifolds (862 citations)
  • Global Minimum for Active Contour Models: A Minimal Path Approach (596 citations)

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

His primary areas of study are Artificial intelligence, Computer vision, Algorithm, Geodesic and Topology. His Artificial intelligence study frequently links to other fields, such as Pattern recognition. His study on Computer vision is mostly dedicated to connecting different topics, such as Computer graphics.

His Algorithm research is multidisciplinary, incorporating elements of Multidimensional scaling, Shape analysis and Mathematical optimization. His study looks at the relationship between Geodesic and fields such as Fast marching method, as well as how they intersect with chemical problems. His work on Topology deals in particular with Embedding and Metric.

He most often published in these fields:

  • Artificial intelligence (48.31%)
  • Computer vision (36.47%)
  • Algorithm (20.05%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (48.31%)
  • Computer vision (36.47%)
  • Algorithm (20.05%)

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

His primary scientific interests are in Artificial intelligence, Computer vision, Algorithm, Deep learning and Face. The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition. As a member of one scientific family, Ron Kimmel mostly works in the field of Computer vision, focusing on Specular reflection and, on occasion, Projector.

The study incorporates disciplines such as Kernel, Linear combination, Manifold, Geodesic and Scaling in addition to Algorithm. His Geodesic research is multidisciplinary, relying on both Nonlinear dimensionality reduction, Multidimensional scaling, Pointwise and Metric. His Deep learning study incorporates themes from Image segmentation, Representation and Invariant.

Between 2015 and 2021, his most popular works were:

  • Learning Detailed Face Reconstruction from a Single Image (229 citations)
  • 3D Face Reconstruction by Learning from Synthetic Data (192 citations)
  • Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation (144 citations)

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

  • Artificial intelligence
  • Computer vision
  • Geometry

His main research concerns Artificial intelligence, Computer vision, Face, Deep learning and Geometric data analysis. His research integrates issues of Machine learning and Pattern recognition in his study of Artificial intelligence. His Computer vision research incorporates themes from Specular reflection and Graphics.

His research in Face intersects with topics in Iterative reconstruction and Robustness. His studies deal with areas such as Image segmentation and Representation as well as Deep learning. His research on Topology often connects related areas such as Algorithm.

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

Geodesic active contours

V. Caselles;R. Kimmel;G. Sapiro.
international conference on computer vision (1995)

4927 Citations

Computing geodesic paths on manifolds

R. Kimmel;J. A. Sethian.
Proceedings of the National Academy of Sciences of the United States of America (1998)

1208 Citations

Global Minimum for Active Contour Models: A Minimal Path Approach

Laurent D. Cohen;Ron Kimmel.
International Journal of Computer Vision (1997)

891 Citations

Numerical geometry of non-rigid shapes

Alexander Bronstein;Michael Bronstein;Ron Kimmel.
(2007)

777 Citations

Three-Dimensional Face Recognition

Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel.
International Journal of Computer Vision (2005)

744 Citations

A Variational Framework for Retinex

Ron Kimmel;Michael Elad;Doron Shaked;Renato Keshet.
International Journal of Computer Vision (2003)

725 Citations

A general framework for low level vision

N. Sochen;R. Kimmel;R. Malladi.
IEEE Transactions on Image Processing (1998)

714 Citations

Demosaicing: image reconstruction from color CCD samples

R. Kimmel.
IEEE Transactions on Image Processing (1999)

694 Citations

Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching

Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel.
Proceedings of the National Academy of Sciences of the United States of America (2006)

641 Citations

On bending invariant signatures for surfaces

A. Elad;R. Kimmel.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2003)

555 Citations

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Best Scientists Citing Ron Kimmel

Laurent D. Cohen

Laurent D. Cohen

Paris Dauphine University

Publications: 109

Daniel Cremers

Daniel Cremers

Technical University of Munich

Publications: 106

Michael M. Bronstein

Michael M. Bronstein

Imperial College London

Publications: 106

Guillermo Sapiro

Guillermo Sapiro

Duke University

Publications: 104

Allen Tannenbaum

Allen Tannenbaum

Stony Brook University

Publications: 92

Anthony Yezzi

Anthony Yezzi

Georgia Institute of Technology

Publications: 89

Nir Sochen

Nir Sochen

Tel Aviv University

Publications: 70

Joachim Weickert

Joachim Weickert

Saarland University

Publications: 67

Alexander M. Bronstein

Alexander M. Bronstein

Technion – Israel Institute of Technology

Publications: 64

Emanuele Rodolà

Emanuele Rodolà

Sapienza University of Rome

Publications: 51

Maks Ovsjanikov

Maks Ovsjanikov

École Polytechnique

Publications: 48

Edwin R. Hancock

Edwin R. Hancock

University of York

Publications: 48

Nikos Paragios

Nikos Paragios

École Centrale Paris

Publications: 47

Stanley Osher

Stanley Osher

University of California, Los Angeles

Publications: 46

Olivier Faugeras

Olivier Faugeras

French Institute for Research in Computer Science and Automation - INRIA

Publications: 45

Leonidas J. Guibas

Leonidas J. Guibas

Stanford University

Publications: 43

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