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[1]
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J. D. B. Nelson, C. Nafornita, and A. Isar.
Semi-local scaling exponent estimation with box-penalty constraints
and total-variation regularization.
IEEE Transactions on Image Processing, 25(7), 2016.
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[2]
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P. Chen, J. D. B. Nelson, and J.-Y. Tourneret.
Towards a sparse Bayesian Markov random field approach to
hyperspectral unmixing and classification.
IEEE Transactions on Image Processing (accepted), 2016.
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[3]
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A. J. Gibberd and J. D. B. Nelson.
Estimating dynamic graphical models from multivariate time-series
data: Recent methods and results.
Lecture Notes on Artificial Intelligence (in press), 2016.
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[4]
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A. J. Gibberd and J. D. B. Nelson.
Regularized estimation of piecewise constant Gaussian graphical
models: The group-fused graphical Lasso.
ArXiv e-print arXiv:1512.06171, 2016.
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[5]
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R. Gaifulina, A. Maher, C. Kendall, J. D. B. Nelson, M. Rodriguez-Justo,
K. Lau, and G. Thomas.
Label-free Raman spectroscopic imaging to extract morphological and
chemical information from a formalin-fixed, paraffin-embedded rat colon
tissue section.
International Journal of Experimental Pathology, 2016.
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[6]
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J. D. B. Nelson and A. J. Gibberd.
Introducing the locally stationary dual-tree complex wavelet model.
IEEE International Conference on Image Processing, 2016.
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[7]
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H. Akhondi-Asl and J. D. B. Nelson.
M-estimate robust PCA for seismic noise attenuation.
IEEE International Conference on Image Processing, 2016.
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[8]
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J.-B. Regli and J. D. B. Nelson.
Scattering convolutional hidden Markov trees.
IEEE International Conference on Image Processing, 2016.
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[9]
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H. Akhondi-Asl and J. D. B. Nelson.
Multi-scale sparse coding with anomaly detection and classification.
IEEE Statistical Signal Processing Workshop, 2016.
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[10]
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A. J. Gibberd and J. D. B. Nelson.
Regularised estimation of 2d-locally stationary wavelet processes.
IEEE Statistical Signal Processing Workshop, 2016.
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[11]
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J. D. B. Nelson.
Enhanced B-wavelets via mixed, composite packets.
IEEE Transactions on Signal Processing, 63(12):3191--3203,
2015.
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[12]
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D. R. Tomassi, D. H. Milone, and J. D. B. Nelson.
Wavelet shrinkage using adaptive structured sparsity constraints
B-wavelets via mixed, composite packets.
Signal Processing, 106:73--87, 2015.
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[13]
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A. J. Gibberd and J. D. B. Nelson.
Estimating multiresolution dependency graphs within the stationary
wavelet framework.
IEEE Global Conference on Signal and Information Processing,
2015.
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[14]
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J. D. B. Nelson, C. Nafornita, and I. Isar.
Generalised M-Lasso for robust, spatially regularised Hurst
estimation.
IEEE Global Conference on Signal and Information Processing,
2015.
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[15]
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N. Tsipinakis and J. D. B. Nelson.
Sparse temporal difference learning via alternating direction method
of multipliers.
IEEE International Conference on Machine Learning and
Applications, 2015.
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[16]
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C. Guo and J. D. B. Nelson.
Compressive imaging with complex wavelet transform and turbo AMP
reconstruction.
Proceedings of the European Signal Processing Conference, 2015.
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[17]
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J.-B. Regli and J. D. B. Nelson.
Piecewise parameterised Markov random fields for semi-local Hurst
estimation.
Proceedings of the European Signal Processing Conference, 2015.
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[18]
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A. J. Gibberd and J. D. B. Nelson.
Sparsity in the multivariate wavelet framework: A comparative study
using epileptic electroencephalography data.
IET Intelligent Signal Processing Conference, 2015.
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[19]
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C. Guo and J. D. B. Nelson.
Direction of arrival estimation via approximate message passing.
IET Intelligent Signal Processing Conference, 2015.
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[20]
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A. J. Gibberd and J. D. B. Nelson.
Estimating dynamic graphical models from multivariate time-series
data.
European Conference on Machine Learning and Principles and
Practice of Knowledge Discovery in Databases: Workshop on Advanced Analytics
and Learning on Temporal Data (ECML/PKDD:AALTD), 2015.
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[21]
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V. Krylov and J. D. B. Nelson.
Line extraction via phase congruency with a novel adaptive scale
selection for poisson noisy medical images.
Proceedings of the V ECCOMAS Thematic Conference on
Computational Vision and Medical Image Processing, 2015.
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[22]
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J. D. B. Nelson.
On the equivalence between a minimal codomain cardinality Riesz
basis, a system of Hadamard-Sylvester operators, and a class of sparse,
binary optimisation problems.
IEEE Transactions on Signal Processing, 62(20):5270--5281,
2014.
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[23]
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V. Krylov and J. D. B. Nelson.
Stochastic extraction of elongated curvilinear structures with
applications.
IEEE Transactions on Image Processing, 23(12):5360--5373, 2014.
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[24]
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A. J. Gibberd and J. D. B. Nelson.
High dimensional changepoint detection with a dynamic graphical
lasso.
IEEE International Conference on Acoustics, Speech, and Signal
Processing, 2014.
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[25]
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C. Nafornita, A. Isar, and J. D. B. Nelson.
Regularised, semi-local Hurst estimation via generalised lasso and
dual-tree complex wavelets.
IEEE International Conference on Image Processing, pages
2689--2693, 2014.
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[26]
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B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson.
Improving hyperspectral band selection by constructing an estimated
reference map.
Journal of Applied Remote Sensing, 8(1), 2014.
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[27]
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J. D. B. Nelson and V. Krylov.
Textural lacunarity for semi-supervised learning in sonar imagery.
IET Radar, Sonar and Navigation, 8(6):616--621, 2014.
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[28]
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A. J. Gibberd and J. D. B. Nelson.
Group-fused graphical lasso for changepoint estimation in
multivariate time-series.
IMA International Conference on Mathematics in Signal
Processing, 2014.
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[29]
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D. Knight-Gaynor, S. Lu, and J. D. B. Nelson.
A sparse region-aware kernel approach to superpixel matching for
histopathological data.
IMA International Conference on Mathematics in Signal
Processing, 2014.
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[30]
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A. Kalaitzis and J. D. B. Nelson.
Online joint classification and anomaly detection via sparse coding.
IEEE Machine Learning for Signal Processing, 2014.
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[31]
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J. D. B. Nelson.
Fused Lasso and rotation invariant autoregressive models for
texture classification.
Pattern Recognition Letters, 34(16):2166--2172, 2013.
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[32]
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V. Krylov and J. D. B. Nelson.
Fast road network extraction from remotely sensed images.
Advanced Concepts for Intelligent Vision Systems, Lecture Notes
in Computer Science, (8192):227--237, 2013.
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[33]
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V. Krylov, S. Taylor, and J. D. B. Nelson.
Stochastic extraction of elongated curvilinear structures in
mammographic images.
International Conference on Image Analysis and Recognition,
2013.
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[34]
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A. J. Gibberd and J. D. B. Nelson.
Sparsity for change point detection in network traffic.
Data Analysis for CyberSecurity, 2013.
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[35]
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J. D. B. Nelson and N. G. Kingsbury.
Fractal dimension, wavelet shrinkage, and anomaly detection for mine
hunting.
IET Signal Processing Journal, 2012.
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[36]
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J. D. B. Nelson and N. G. Kingsbury.
Multiresolution Markov random field wavelet shrinkage for ripple
suppression in sonar imagery.
IMA International Conference on Mathematics in Signal
Processing, 2012.
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[37]
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S. Julier, R. De Nardi, and J. D. B. Nelson.
Multi-rate estimation of coloured noise models in graph-based
estimation algorithms.
International Conference on Information Fusion, 2012.
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[38]
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J. D. B. Nelson and N. G. Kingsbury.
Enhanced shift and scale tolerance for dual-tree complex wavelet
rotation invariant matching.
IEEE Transactions on Image Processing, 20(3), 2011.
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[39]
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J. D. B. Nelson and N. G. Kingsbury.
Fractal dimension based sand ripple suppression for mine hunting with
sidescan sonar.
International Conference on Synthetic Aperture Sonar and
Synthetic Aperture Radar, 2010.
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[40]
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J. D. B. Nelson and N. G. Kingsbury.
Dual-tree wavelets for locally varying and anisotropic fractal
dimension estimation.
IEEE International Conference on Image Processing, 2010.
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[41]
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J. D. B. Nelson, S. R. Gunn, R. I. Damper, and B. Guo.
A signal theory approach to support vector classification: the sinc
kernel.
Neural Networks, 22(1):49--57, 2009.
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[42]
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S. K. Pang, J. D. B. Nelson, S. J. Godsill, and N. G. Kingsbury.
Video tracking using dual-tree wavelet polar matching and
rao-blackwellised particle filter.
Journal on Advances in Signal Processing, 2009.
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[43]
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J. D. B. Nelson, S. R. Gunn, R. I. Damper, and B. Guo.
Signal theory for SVM kernel design with applications to parameter
estimation and sequence kernels.
Neurocomputing, 72(1-3):15--22, 2009.
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[44]
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B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson.
A fast separability-based feature selection method for
highdimensional remotely-sensed image classification.
Pattern Recognition, 41(8), 2008.
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[45]
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B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson.
Customizing kernel functions for SVM-based hyperspectral image
classification.
IEEE Transactions on Image Processing, 17(4):622--629, 2008.
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[46]
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J. D. B. Nelson, S. K. Pang, S. J. Godsill, and N. G. Kingsbury.
Tracking ground based targets in aerial video with dual-tree complex
wavelet polar matching and particle filtering.
International Conference on Information Fusion, 2008.
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[47]
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S. K. Pang, J. D. B. Nelson, S. J. Godsill, and N. G. Kingsbury.
Video tracking using dual-tree wavelet polar matching and particle
filtering.
The Institution of Engineering and Technology Seminar on Target
Tracking and Data Fusion, 2009.
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[48]
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A. Mahmood, P. M. Tudor, W. Oxford, R. Hansford, J. D. B. Nelson, N. G.
Kingsbury, A. Katartzis, M. Petrou, N. Mitianoudis, T. Stathaki, A. Achim,
D. Bull, N. Canagarajah, S. Nikolov, A. Loza, and N. Cvejic.
Applied multi-dimensional fusion.
The Computer Journal, 50(6), 2007.
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[49]
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B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson.
Band selection for hyperspectral image classification using mutual
information.
IEEE Geoscience and Remote Sensing Letters, 2006.
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[50]
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J. D. B. Nelson, S. R. Gunn, R. I. Damper, and B. Guo.
Signal theory for SVM kernel parameter estimation.
Proceedings of IEEE International Workshop on Machine Learning
for Signal Processing, 2006.
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[51]
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J. D. B. Nelson.
A wavelet filter enhancement scheme with a fast integral B-wavelet
transform and pyramidal multi-B wavelet algorithm.
Applied and Computational Harmonic Analysis, 18(3):234--251,
2005.
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[52]
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B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson.
Adaptive band selection for hyperspectral image fusion using mutual
information.
International Conference on Information Fusion, 2005.
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[53]
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B. Guo, S. R. Gunn, R. I. Damper, and J. D. B. Nelson.
Hyperspectral image fusion using spectrally weighted kernels.
International Conference on Information Fusion, 2005.
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[54]
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J. D. B. Nelson.
Applications of enhanced vision to the automotive industry.
SMi Conference on Enhanced Vision, 2004.
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[55]
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J. D. B. Nelson.
On the coefficient quantization of the Fourier basis.
IEEE Transactions on Signal Processing, 51(7):1838--1846, 2003.
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[56]
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J. D. B. Nelson.
A multi-channel, multi-sampling rate theorem.
Sampling Theory in Signal and Image Processing: An International
Journal, 2(1):83--96, 2003.
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[57]
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J. D. B. Nelson.
The construction of some Riesz basis families and their application
to coefficient quantization, sampling theory, and wavelet analysis.
PhD thesis, Anglia Polytechnic University, 2001.
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