Arman Melkumyan
Arman Melkumyan
Senior Research Fellow and Orebody Knowledge Theme Leader, Rio Tinto Centre for Mine
Verified email at acfr.usyd.edu.au
Title
Cited by
Cited by
Year
A sparse covariance function for exact Gaussian process inference in large datasets.
A Melkumyan, F Ramos
IJCAI 9, 1936-1942, 2009
742009
Multi-kernel Gaussian processes
A Melkumyan, F Ramos
Twenty-second international joint conference on artificial intelligence, 2011
672011
Twelve shear surface waves guided by clamped/free boundaries in magneto-electro-elastic materials
A Melkumyan
International Journal of Solids and Structures 44 (10), 3594-3599, 2007
602007
Influence of imperfect bonding on interface waves guided by piezoelectric/piezomagnetic composites
A Melkumyan, YW Mai
Philosophical Magazine 88 (23), 2965-2977, 2008
532008
On the linear and nonlinear observability analysis of the SLAM problem
LDL Perera, A Melkumyan, E Nettleton
2009 IEEE International Conference on Mechatronics, 1-6, 2009
382009
Pretraining for hyperspectral convolutional neural network classification
L Windrim, A Melkumyan, RJ Murphy, A Chlingaryan, R Ramakrishnan
IEEE Transactions on Geoscience and Remote Sensing 56 (5), 2798-2810, 2018
292018
A novel spectral unmixing method incorporating spectral variability within endmember classes
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan
IEEE Transactions on Geoscience and Remote Sensing 54 (5), 2812-2831, 2015
292015
Evaluating the performance of a new classifier–the GP-OAD: A comparison with existing methods for classifying rock type and mineralogy from hyperspectral imagery
S Schneider, RJ Murphy, A Melkumyan
ISPRS journal of photogrammetry and remote sensing 98, 145-156, 2014
262014
An observation angle dependent nonstationary covariance function for Gaussian process regression
A Melkumyan, E Nettleton
International Conference on Neural Information Processing, 331-339, 2009
242009
A novel endmember bundle extraction and clustering approach for capturing spectral variability within endmember classes
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan
IEEE Transactions on Geoscience and Remote Sensing 54 (11), 6712-6731, 2016
192016
Incorporating spatial information and endmember variability into unmixing analyses to improve abundance estimates
T Uezato, RJ Murphy, A Melkumyan, A Chlingaryan
IEEE Transactions on Image Processing 25 (12), 5563-5575, 2016
172016
Automated recognition of stratigraphic marker shales from geophysical logs in iron ore deposits
K Silversides, A Melkumyan, D Wyman, P Hatherly
Computers & Geosciences 77, 118-125, 2015
172015
A physics-based deep learning approach to shadow invariant representations of hyperspectral images
L Windrim, R Ramakrishnan, A Melkumyan, RJ Murphy
IEEE Transactions on Image Processing 27 (2), 665-677, 2017
162017
Gaussian processes with OAD covariance function for hyperspectral data classification
S Schneider, A Melkumyan, RJ Murphy, E Nettleton
2010 22nd IEEE International Conference on Tools with Artificial …, 2010
152010
Method and system of data modelling
A Melkumyan, FT Ramos
US Patent 8,849,622, 2014
132014
Hyperspectral CNN classification with limited training samples
L Windrim, R Ramakrishnan, A Melkumyan, R Murphy
arXiv preprint arXiv:1611.09007, 2016
102016
t-SNE based visualisation and clustering of geological domain
M Balamurali, A Melkumyan
International Conference on Neural Information Processing, 565-572, 2016
92016
Unsupervised feature-learning for hyperspectral data with autoencoders
L Windrim, R Ramakrishnan, A Melkumyan, RJ Murphy, A Chlingaryan
Remote Sensing 11 (7), 864, 2019
82019
A Dynamic Time Warping based covariance function for Gaussian Processes signature identification
KL Silversides, A Melkumyan
Computers & Geosciences 96, 69-76, 2016
72016
Unsupervised feature learning for illumination robustness
L Windrim, A Melkumyan, R Murphy, A Chlingaryan, J Nieto
2016 IEEE International Conference on Image Processing (ICIP), 4453-4457, 2016
72016
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