Edwin Hancock | University of York (original) (raw)

Edwin Hancock

Edwin Hancock holds a B.Sc. degree in physics (1977), a Ph.D. degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham. From 1981–1991 he worked as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). During this period, he also held adjunct teaching posts at the University of Surrey and the Open University. In 1991, he moved to the University of York as a lecturer in the Department of Computer Science, where he has held a chair in Computer Vision since 1998. He leads a group of some 25 faculty, research staff, and Ph.D. students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical pattern recognition. He is currently working on graph matching, shape-from-X, image databases, and statistical learning theory. His work has found applications in areas such as radar terrain analysis, seismic section analysis, remote sensing, and medical imaging. He has published about 135 journal papers and 500 refereed conference publications. He was awarded the Pattern Recognition Society medal in 1991 and an outstanding paper award in 1997 by the journal Pattern Recognition. He has also received best paper prizes at CAIP 2001, ACCV 2002, ICPR 2006, BMVC 2007 and ICIAP 2009. In 2009 he was awarded a Royal Society Wolfson Research Merit Award. In 1998, he became a fellow of the International Association for Pattern Recognition. He is also a fellow of the Institute of Physics, the Institute of Engineering and Technology, and the British Computer Society. He has been a member of the editorial boards of the journals IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition, Computer Vision and Image Understanding, and Image and Vision Computing. In 2006, he was appointed as the founding editor-in-chief of the IET Computer Vision Journal. He has been conference chair for BMVC 1994, Track Chair for ICPR 2004 and Area Chair for ECCV 2006 and CVPR 2008, and in 1997 he established the EMMCVPR workshop series.
Phone: 44 1904 325497
Address: Department of Computer Science,
University of York,
Deramore Lane,
York YO10 5GH, UK.

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Books by Edwin Hancock

Research paper thumbnail of Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings Springer 2010

Papers by Edwin Hancock

Research paper thumbnail of Graph Similarity through Entropic Manifold Alignment

SIAM Journal on Imaging Sciences, 2017

Research paper thumbnail of A Hierarchical Framework for Spectral Correspondence

Lecture Notes in Computer Science, 2002

Research paper thumbnail of Graph characteristic from the Gauss-Bonnet theorem

Research paper thumbnail of Combining global, regional and contextual features for automatic image annotation

Pattern Recognition, 2009

Research paper thumbnail of Energy Minimization Methods in Computer Vision and Pattern Recognition

Lecture Notes in Computer Science, 2013

Research paper thumbnail of Perceptual grouping using eigendecomposition and the EM algorithm

PROCEEDINGS OF THE SCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS, 2001

Research paper thumbnail of Discrimination of thermally-marked otoliths from unmarked specimens by machine learning of texture characteristics

Research paper thumbnail of IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Recovery of Surface Orientation from Diffuse

— When unpolarized light is reflected from a smooth dielectric surface, it becomes partially pola... more — When unpolarized light is reflected from a smooth dielectric surface, it becomes partially polarized. This is due to the orientation of dipoles induced in the reflecting medium and applies to both specular and diffuse reflection. This paper is concerned with exploiting polarization by surface reflection, using images of smooth dielectric objects, to recover surface normals and hence height. The paper presents the underlying physics of polarization by reflection, starting with the Fresnel equations. These equations are used to interpret images taken with a linear polarizer and digital camera, revealing the shape of the objects. Experimental results are presented that illustrate that the technique is accurate near object limbs, as the theory predicts, with less precise, but still useful, results elsewhere. A detailed analysis of the accuracy of the technique for a variety of materials is presented. A method for estimating refractive indices using a laser and linear polarizer is also...

Research paper thumbnail of Local Binary Patterns for Graph Characterization

2018 24th International Conference on Pattern Recognition (ICPR), 2018

Research paper thumbnail of Appearance-based object recognition using shape-from-shading

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170)

Research paper thumbnail of Bragg Diffraction Patterns as Graph Characteristics

Energy Minimization Methods in Computer Vision and Pattern Recognition, 2018

Research paper thumbnail of Shape from Diffuse Polarisation

british machine vision conference, 2004

Research paper thumbnail of Riemannian Graph Diffusion for DT-MRI Regularization

Lecture Notes in Computer Science, 2006

Research paper thumbnail of Tensor MRI Regularization via Graph Diffusion

Research paper thumbnail of A Robust Graph Partition Method from the Path-Weighted Adjacency Matrix

Lecture Notes in Computer Science, 2005

In this paper we develop a new graph representation based on the path-weighted adjacency matrix f... more In this paper we develop a new graph representation based on the path-weighted adjacency matrix for characterising global graph structure. The representation is derived from the heat-kernel of the graph. We investigate whether the path-weighted adjacency matrix can be used for the problem of graph partitioning. Here we demonstrate that the method out-performs the use of the adjacency matrix. The main advantage of the new method is that it both preserves partition consistency and shows improved stability to structural error.

Research paper thumbnail of Increased Extent of Characteristic Views using Shape-from-Shading for Object Recognition

Research paper thumbnail of Grouping Line-segments using Eigenclustering

Procedings of the British Machine Vision Conference 2000, 2000

Research paper thumbnail of Smoothing Tensor-Valued Images Using Anisotropic Geodesic Diffusion

Lecture Notes in Computer Science, 2006

Research paper thumbnail of Graph Spectral Image Smoothing

Lecture Notes in Computer Science

A new method for smoothing both gray-scale and color images is presented that relies on the heat ... more A new method for smoothing both gray-scale and color images is presented that relies on the heat diffusion equation on a graph. We represent the image pixel lattice using a weighted undirected graph. The edge weights of the graph are determined by the Gaussian weighted distances between local neighbouring windows. We then compute the associated Laplacian matrix (the degree matrix minus the adjacency matrix). Anisotropic diffusion across this weighted graph-structure with time is captured by the heat equation, and the ...

Research paper thumbnail of Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR&SPR 2010, Cesme, Izmir, Turkey, August 18-20, 2010. Proceedings Springer 2010

Research paper thumbnail of Graph Similarity through Entropic Manifold Alignment

SIAM Journal on Imaging Sciences, 2017

Research paper thumbnail of A Hierarchical Framework for Spectral Correspondence

Lecture Notes in Computer Science, 2002

Research paper thumbnail of Graph characteristic from the Gauss-Bonnet theorem

Research paper thumbnail of Combining global, regional and contextual features for automatic image annotation

Pattern Recognition, 2009

Research paper thumbnail of Energy Minimization Methods in Computer Vision and Pattern Recognition

Lecture Notes in Computer Science, 2013

Research paper thumbnail of Perceptual grouping using eigendecomposition and the EM algorithm

PROCEEDINGS OF THE SCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS, 2001

Research paper thumbnail of Discrimination of thermally-marked otoliths from unmarked specimens by machine learning of texture characteristics

Research paper thumbnail of IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Recovery of Surface Orientation from Diffuse

— When unpolarized light is reflected from a smooth dielectric surface, it becomes partially pola... more — When unpolarized light is reflected from a smooth dielectric surface, it becomes partially polarized. This is due to the orientation of dipoles induced in the reflecting medium and applies to both specular and diffuse reflection. This paper is concerned with exploiting polarization by surface reflection, using images of smooth dielectric objects, to recover surface normals and hence height. The paper presents the underlying physics of polarization by reflection, starting with the Fresnel equations. These equations are used to interpret images taken with a linear polarizer and digital camera, revealing the shape of the objects. Experimental results are presented that illustrate that the technique is accurate near object limbs, as the theory predicts, with less precise, but still useful, results elsewhere. A detailed analysis of the accuracy of the technique for a variety of materials is presented. A method for estimating refractive indices using a laser and linear polarizer is also...

Research paper thumbnail of Local Binary Patterns for Graph Characterization

2018 24th International Conference on Pattern Recognition (ICPR), 2018

Research paper thumbnail of Appearance-based object recognition using shape-from-shading

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170)

Research paper thumbnail of Bragg Diffraction Patterns as Graph Characteristics

Energy Minimization Methods in Computer Vision and Pattern Recognition, 2018

Research paper thumbnail of Shape from Diffuse Polarisation

british machine vision conference, 2004

Research paper thumbnail of Riemannian Graph Diffusion for DT-MRI Regularization

Lecture Notes in Computer Science, 2006

Research paper thumbnail of Tensor MRI Regularization via Graph Diffusion

Research paper thumbnail of A Robust Graph Partition Method from the Path-Weighted Adjacency Matrix

Lecture Notes in Computer Science, 2005

In this paper we develop a new graph representation based on the path-weighted adjacency matrix f... more In this paper we develop a new graph representation based on the path-weighted adjacency matrix for characterising global graph structure. The representation is derived from the heat-kernel of the graph. We investigate whether the path-weighted adjacency matrix can be used for the problem of graph partitioning. Here we demonstrate that the method out-performs the use of the adjacency matrix. The main advantage of the new method is that it both preserves partition consistency and shows improved stability to structural error.

Research paper thumbnail of Increased Extent of Characteristic Views using Shape-from-Shading for Object Recognition

Research paper thumbnail of Grouping Line-segments using Eigenclustering

Procedings of the British Machine Vision Conference 2000, 2000

Research paper thumbnail of Smoothing Tensor-Valued Images Using Anisotropic Geodesic Diffusion

Lecture Notes in Computer Science, 2006

Research paper thumbnail of Graph Spectral Image Smoothing

Lecture Notes in Computer Science

A new method for smoothing both gray-scale and color images is presented that relies on the heat ... more A new method for smoothing both gray-scale and color images is presented that relies on the heat diffusion equation on a graph. We represent the image pixel lattice using a weighted undirected graph. The edge weights of the graph are determined by the Gaussian weighted distances between local neighbouring windows. We then compute the associated Laplacian matrix (the degree matrix minus the adjacency matrix). Anisotropic diffusion across this weighted graph-structure with time is captured by the heat equation, and the ...

Research paper thumbnail of Image Scale-Space from the Heat Kernel

Lecture Notes in Computer Science, 2005

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