H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 57 Citations 12,779 599 World Ranking 1885 National Ranking 104

Research.com Recognitions

Awards & Achievements

2022 - Fellow of the Asia-Pacific Artificial Inteliegence Association

2021 - Fellow of the Royal Academy of Engineering (UK)

2018 - IAPR Pierre Devijver Award, International Association for Pattern Recognition

2016 - Distinguished Fellow of the British Machine Vision Association (BMVA)

2016 - IEEE Fellow For contributions to pattern recognition and computer vision

2008 - Fellow of the Institution of Engineering and Technology (IET), UK

2006 - IAPR P. Zamperoni Award A Reimannian Weighted Filter for Edge-sensitive Image Smoothing

2000 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to structural and statistical pattern recognition, and to computer vision

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Computer vision

Edwin R. Hancock mainly investigates Artificial intelligence, Algorithm, Combinatorics, Discrete mathematics and Computer vision. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Curvature and Pattern recognition. His Algorithm study incorporates themes from Probabilistic logic, Graph theory and Mathematical optimization.

Cluster analysis is closely connected to Edit distance in his research, which is encompassed under the umbrella topic of Combinatorics. The Line graph and Graph energy research he does as part of his general Discrete mathematics study is frequently linked to other disciplines of science, such as Ihara zeta function, therefore creating a link between diverse domains of science. His Computer vision research includes themes of Normal and Statistical model.

His most cited work include:

  • Structural, syntactic, and statistical pattern recognition (446 citations)
  • Structural graph matching using the EM algorithm and singular value decomposition (310 citations)
  • Structural matching by discrete relaxation (289 citations)

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

Edwin R. Hancock mainly focuses on Artificial intelligence, Pattern recognition, Computer vision, Algorithm and Discrete mathematics. He regularly links together related areas like Surface in his Artificial intelligence studies. His studies in Pattern recognition integrate themes in fields like Graph, Feature and Expectation–maximization algorithm.

His work in Algorithm covers topics such as Graph which are related to areas like Theoretical computer science. His Discrete mathematics research is multidisciplinary, incorporating perspectives in Graph theory and Combinatorics. His Adjacency matrix study combines topics from a wide range of disciplines, such as Degree matrix, Spectral graph theory, Adjacency list and Graph energy.

He most often published in these fields:

  • Artificial intelligence (50.33%)
  • Pattern recognition (26.20%)
  • Computer vision (20.00%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (50.33%)
  • Pattern recognition (26.20%)
  • Graph (7.83%)

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

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Graph, Theoretical computer science and Combinatorics. The concepts of his Artificial intelligence study are interwoven with issues in Algorithm, Graph and Computer vision. His work deals with themes such as Matrix, Image and Partial derivative, which intersect with Algorithm.

His Pattern recognition research is multidisciplinary, relying on both Subspace topology, Similarity, Feature and Image retrieval. His research in Theoretical computer science intersects with topics in Representation, Grid, Vertex, Convolutional neural network and Graph kernel. The Combinatorics study combines topics in areas such as Discrete mathematics, Quantum walk, Kernel embedding of distributions, Tree kernel and Statistical physics.

Between 2014 and 2021, his most popular works were:

  • A quantum Jensen-Shannon graph kernel for unattributed graphs (63 citations)
  • Adaptive hash retrieval with kernel based similarity (44 citations)
  • Joint hypergraph learning and sparse regression for feature selection (40 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of study are Artificial intelligence, Pattern recognition, Combinatorics, Graph and Theoretical computer science. His Artificial intelligence research is multidisciplinary, incorporating elements of Binary code and Machine learning. Edwin R. Hancock combines subjects such as Distribution and Subspace clustering with his study of Pattern recognition.

His Combinatorics research incorporates elements of Discrete mathematics, Jensen–Shannon divergence, Kernel embedding of distributions, Graph kernel and Quantum computer. The various areas that Edwin R. Hancock examines in his Graph study include Optimization problem, Graph and Cluster analysis. His Theoretical computer science study integrates concerns from other disciplines, such as Grid and Convolutional neural network.

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

Structural, syntactic, and statistical pattern recognition

Edwin R. Hancock;Richard C. Wilson;Terry Windeatt;Ilkay Ulusoy.
Lecture Notes in Computer Science (2002)

699 Citations

Structural graph matching using the EM algorithm and singular value decomposition

Bin Luo;E.R. Hancock.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2001)

467 Citations

Structural matching by discrete relaxation

R.C. Wilson;E.R. Hancock.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1997)

424 Citations

Spectral embedding of graphs

Bin Luo;Bin Luo;Richard C. Wilson;Edwin R. Hancock.
Pattern Recognition (2003)

349 Citations

Graph matching with a dual-step EM algorithm

A.D.J. Cross;E.R. Hancock.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1998)

302 Citations

Clustering and Embedding Using Commute Times

Huaijun Qiu;E.R. Hancock.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)

289 Citations

Pattern vectors from algebraic graph theory

R.C. Wilson;E.R. Hancock;Bin Luo.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

288 Citations

New constraints on data-closeness and needle map consistency for shape-from-shading

P.L. Worthington;E.R. Hancock.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1999)

288 Citations

Graph edit distance from spectral seriation

A. Robles-Kelly;E.R. Hancock.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)

273 Citations

Spectral correspondence for point pattern matching

Marco Carcassoni;Edwin R. Hancock.
Pattern Recognition (2003)

271 Citations

Editorial Boards

Pattern Recognition
(Impact Factor: 8.518)

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