D-Index & Metrics Best Publications
Daniel P. Huttenlocher

Daniel P. Huttenlocher

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 69 Citations 40,914 115 World Ranking 893 National Ranking 539

Research.com Recognitions

Awards & Achievements

2007 - ACM Fellow For contributions to computer vision.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Statistics

Daniel P. Huttenlocher spends much of his time researching Artificial intelligence, Computer vision, Pattern recognition, Structure and Social network. His work deals with themes such as Machine learning and Hausdorff dimension, which intersect with Artificial intelligence. His work on Image, Feature, Structure from motion and Markov random field as part of general Computer vision study is frequently linked to Continuous optimization, bridging the gap between disciplines.

His studies deal with areas such as Image processing and Hausdorff distance as well as Pattern recognition. The concepts of his Social network study are interwoven with issues in Social relation, Social psychology, Social psychology and Cognitive psychology. His Image texture research is multidisciplinary, incorporating elements of Scale-space segmentation and Segmentation-based object categorization.

His most cited work include:

  • Efficient Graph-Based Image Segmentation (4870 citations)
  • Comparing images using the Hausdorff distance (3150 citations)
  • Pictorial Structures for Object Recognition (2033 citations)

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

His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Image and Algorithm. His study explores the link between Artificial intelligence and topics such as Natural language processing that cross with problems in Speech recognition. His work on Image texture as part of general Pattern recognition study is frequently connected to Maximum a posteriori estimation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.

While the research belongs to areas of Image, he spends his time largely on the problem of Information retrieval, intersecting his research to questions surrounding Code and Character. His Algorithm research incorporates elements of Affine arithmetic, Affine shape adaptation, Affine combination, Geometric hashing and Topology. His work carried out in the field of Object brings together such families of science as Transformation, Motion and Pattern recognition.

He most often published in these fields:

  • Artificial intelligence (56.73%)
  • Computer vision (29.82%)
  • Pattern recognition (23.98%)

What were the highlights of his more recent work (between 2008-2016)?

  • Artificial intelligence (56.73%)
  • Computer vision (29.82%)
  • Social media (4.68%)

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

Artificial intelligence, Computer vision, Social media, Data science and Social network are his primary areas of study. In his works, Daniel P. Huttenlocher performs multidisciplinary study on Artificial intelligence and Global Positioning System. In his study, which falls under the umbrella issue of Computer vision, Orientation, Kernel and Image restoration is strongly linked to Computer graphics.

His Data science study combines topics in areas such as Scale and Presentation. His Social network study also includes fields such as

  • Social psychology that connect with fields like Social computing,
  • Social psychology and related Perspective, Cognitive psychology and Structure. Daniel P. Huttenlocher studied Feature and Pattern recognition that intersect with Image retrieval, Automatic image annotation, Visual Word and Image registration.

Between 2008 and 2016, his most popular works were:

  • Predicting positive and negative links in online social networks (1043 citations)
  • Signed networks in social media (822 citations)
  • Mapping the world's photos (733 citations)

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

  • Artificial intelligence
  • Computer vision
  • Statistics

His primary areas of investigation include Social media, Artificial intelligence, Structure, Data science and Social psychology. His Social media research includes themes of Key and Human–computer interaction. His work investigates the relationship between Artificial intelligence and topics such as Computer vision that intersect with problems in Pattern recognition.

The study incorporates disciplines such as Geotagging, Information retrieval and Geolocation in addition to Structure. His Data science study combines topics in areas such as Question answering, World Wide Web and Product. In his work, Social relation, Friendship and Variety is strongly intertwined with Social network, which is a subfield of Social psychology.

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

Efficient Graph-Based Image Segmentation

Pedro F. Felzenszwalb;Daniel P. Huttenlocher.
International Journal of Computer Vision (2004)

7269 Citations

Comparing images using the Hausdorff distance

D.P. Huttenlocher;G.A. Klanderman;W.J. Rucklidge.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1993)

5166 Citations

Pictorial Structures for Object Recognition

Pedro F. Felzenszwalb;Daniel P. Huttenlocher.
International Journal of Computer Vision (2005)

2810 Citations

Efficient Belief Propagation for Early Vision

Pedro F. Felzenszwalb;Daniel P. Huttenlocher.
International Journal of Computer Vision (2006)

2305 Citations

Group formation in large social networks: membership, growth, and evolution

Lars Backstrom;Dan Huttenlocher;Jon Kleinberg;Xiangyang Lan.
knowledge discovery and data mining (2006)

2166 Citations

Predicting positive and negative links in online social networks

Jure Leskovec;Daniel Huttenlocher;Jon Kleinberg.
the web conference (2010)

1244 Citations

An efficiently computable metric for comparing polygonal shapes

E.M. Arkin;L.P. Chew;D.P. Huttenlocher;K. Kedem.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1991)

1018 Citations

Distance Transforms of Sampled Functions

Pedro F. Felzenszwalb;Daniel P. Huttenlocher.
Theory of Computing (2012)

988 Citations

Mapping the world's photos

David J. Crandall;Lars Backstrom;Daniel Huttenlocher;Jon Kleinberg.
the web conference (2009)

986 Citations

Signed networks in social media

Jure Leskovec;Daniel Huttenlocher;Jon Kleinberg.
human factors in computing systems (2010)

906 Citations

Best Scientists Citing Daniel P. Huttenlocher

Andrew Zisserman

Andrew Zisserman

University of Oxford

Publications: 76

Philip H. S. Torr

Philip H. S. Torr

University of Oxford

Publications: 73

Jie Tang

Jie Tang

Tsinghua University

Publications: 66

Marc Pollefeys

Marc Pollefeys

ETH Zurich

Publications: 58

Jure Leskovec

Jure Leskovec

Stanford University

Publications: 57

Huan Liu

Huan Liu

Arizona State University

Publications: 53

Luc Van Gool

Luc Van Gool

ETH Zurich

Publications: 51

Christos Faloutsos

Christos Faloutsos

Carnegie Mellon University

Publications: 50

Torsten Sattler

Torsten Sattler

Czech Technical University in Prague

Publications: 50

Martial Hebert

Martial Hebert

Carnegie Mellon University

Publications: 49

Jitendra Malik

Jitendra Malik

University of California, Berkeley

Publications: 47

Josef Sivic

Josef Sivic

Czech Technical University in Prague

Publications: 44

Larry S. Davis

Larry S. Davis

University of Maryland, College Park

Publications: 44

Reinhard Klette

Reinhard Klette

Auckland University of Technology

Publications: 42

Martin T. King

Martin T. King

Google (United States)

Publications: 41

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

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