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• journal articles and conference papers
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• theses
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Journal Articles & Conference Papers

• Resolving Ethics Trade-offs in Implementing Responsible AI
  C. Sanderson, E. Schleiger, D. Douglas, P. Kuhnert, Q. Lu.
  preprint, 2024.
  arXiv: 2401.08103

• A Neural Emulator for Uncertainty Estimation of Fire Propagation
  A. Bolt, C. Sanderson. J. Dabrowski, C. Huston, P. Kuhnert.
  Procedia Computer Science, Vol. 222, 2023.
  doi: 10.1016/j.procs.2023.08.175 | sciencedirect | arXiv: 2305.06139

• Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects
  C. Sanderson, D. Douglas, Q. Lu.
  International Joint Conference on Neural Networks (IJCNN), 2023.
  doi: 10.1109/IJCNN54540.2023.10191274 | ieeexplore: 10191274 | arXiv: 2304.08275

• AI Ethics Principles in Practice: Perspectives of Designers and Developers
  C. Sanderson, D. Douglas, Q. Lu, E. Schleiger, J. Whittle, J. Lacey, G. Newnham, S. Hajkowicz, C. Robinson, D. Hansen.
  IEEE Transactions on Technology and Society, Vol. 4, No. 2, 2023.
  doi: 10.1109/TTS.2023.3257303 | ieeexplore: 10071542 | arXiv: 2112.07467

• Solving travelling thief problems using coordination based methods
  M. Namazi, M.A.H. Newton, C. Sanderson, A. Sattar.
  Journal of Heuristics, 2023
  doi: 10.1007/s10732-023-09518-7 | arXiv: 2310.07156

• Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021
  S. Hajkowicz, C. Sanderson, S. Karimi, A. Bratanova, C. Naughtin.
  Technology in Society, Vol. 74, 2023.
  doi: 10.1016/j.techsoc.2023.102260 | arXiv: 2306.09145

• mlpack 4: a fast, header-only C++ machine learning library
  R. Curtin, M. Edel, O. Shrit, (...), C. Sanderson.
  Journal of Open Source Software, Vol. 8, No. 82, 2023.
  doi: 10.21105/joss.05026 | arXiv: 2302.00820 | mlpack.org

• Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires
  J. Dabrowski, D. Pagendam, J. Hilton, C. Sanderson, D. MacKinlay, C. Huston, A. Bolt, P. Kuhnert
  Spatial Statistics, Vol. 55, 2023.
  doi: 10.1016/j.spasta.2023.100746 | arXiv: 2212.00970

• Artificial Intelligence for Science - Adoption Trends and Future Development Pathways
  S. Hajkowicz, C. Naughtin, C. Sanderson, E. Schleiger, S. Karimi, A. Bratanova, T. Bednarz.
  Report, Data61/CSIRO, 2022.

• Towards Implementing Responsible AI
  C. Sanderson, Q. Lu, D. Douglas, X. Xu, L. Zhu, J. Whittle.
  IEEE Big Data Conference, 2022
  doi: 10.1109/BigData55660.2022.10021121 | arXiv: 2205.04358

• A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models
  A. Bolt, C. Huston, P. Kuhnert, J. Dabrowski, J. Hilton, C. Sanderson.
  IEEE Conference on Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2022
  doi: 10.23919/SPA53010.2022.9927888 | arXiv: 2206.08523

• Reducing Object Detection Uncertainty from RGB and Thermal Data for UAV Outdoor Surveillance
  J. Sandino, P. Caccetta, C. Sanderson, F. Maire, F. Gonzalez.
  IEEE Aerospace Conference, 2022.
  doi: AERO53065.2022.9843611 | arXiv: 2308.10671

• Software engineering for Responsible AI
  Q. Lu, L. Zhu, X. Xu, J. Whittle, D. Douglas, C. Sanderson.
  IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), 2022.
  doi: 10.1109/ICSE-SEIP55303.2022.9793864

• Opportunistic Emulation of Computationally Expensive Simulations via Deep Learning
  C. Sanderson, D. Pagendam, B. Power, F. Bennett, R. Darnell.
  International Congress on Modelling and Simulation (MODSIM), 2021.
  doi: 10.36334/modsim.2021.L1.sanderson | arXiv: 2108.11057

• Drone-Based Autonomous Motion Planning System for Outdoor Environments under Object Detection Uncertainty
  J. Sandino, F. Maire, P. Caccetta, C. Sanderson, F. Gonzalez.
  Remote Sensing, Vol. 13, No. 21, 2021.
  doi: 10.3390/rs13214481

• The ensmallen library for flexible numerical optimization
  R. Curtin, M. Edel, R.G. Prabhu, S. Basak, Z. Lou, C. Sanderson.
  Journal of Machine Learning Research, Vol. 22, No. 166, 2021.
  JMLR: v22/20-416 | ACM Digital Library | arxiv: 2108.12981

• PyArmadillo: a streamlined linear algebra library for Python
  Jason Rumengan, Terry Yue Zhuo, Conrad Sanderson.
  Journal of Open Source Software, Vol. 6, No. 66, 2021.
  doi: 10.21105/joss.03051 | arXiv: 2104.11120 | pyarma.sourceforge.io

• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments
  X. Zhu, F. Vanegas, F. Gonzalez, C. Sanderson.
  International Conference on Unmanned Aircraft Systems, 2021.
  doi: 10.1109/ICUAS51884.2021.9476820 | arXiv: 2107.08834

• UAV Framework for Autonomous Onboard Navigation and People/Object Detection in Cluttered Indoor Environments
  J. Sandino, F. Vanegas, F. Maire, P. Caccetta, C. Sanderson, F. Gonzalez.
  Remote Sensing, Vol. 12, No. 20, 2020.
  doi: 10.3390/rs12203386

• An Adaptive Solver for Systems of Linear Equations
  C. Sanderson, R. Curtin.
  International Conference on Signal Processing and Communication Systems, 2020.
  doi: ICSPCS50536.2020.9309998 | arXiv: 2007.11208

• Surrogate Assisted Optimisation for Travelling Thief Problems
  M. Namazi, C. Sanderson, M.A.H. Newton, A. Sattar.
  Symposium on Combinatorial Search (SoCS), 2020.
  doi: 10.1609/socs.v11i1.18542 | arXiv: 2005.06695

• Flexible numerical optimization with ensmallen
  R. Curtin, M. Edel, R. Prabhu, S. Basak, Z. Lou, C. Sanderson.
  arXiv: 2003.04103

• A Cooperative Coordination Solver for Travelling Thief Problems
  M. Namazi, C. Sanderson, M.A.H. Newton, A. Sattar.
  arXiv: 1911.03124

• Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation
  C. Sanderson, R. Curtin.
  Mathematical and Computational Applications, Vol. 24, No. 3, 2019.
  doi: 10.3390/mca24030070

• A Profit Guided Coordination Heuristic for Travelling Thief Problems
  M. Namazi, M.A.H. Newton, A. Sattar, C. Sanderson.
  Symposium on Combinatorial Search (SoCS), 2019.
  doi: 10.1609/socs.v10i1.18513 | doi: 10.5281/zenodo.3346237

• ensmallen: a flexible C++ library for efficient function optimization
  S. Bhardwaj, R. Curtin, M. Edel, Y. Mentekidis, C. Sanderson.
  Workshop on Systems for ML and Open Source Software at NIPS 2018.
  doi: 10.5281/zenodo.2008650 | arXiv: 1810.09361

• Diversified Late Acceptance Search
  M. Namazi, C. Sanderson, M.A.H. Newton, M.M.A. Polash, A. Sattar.
  Lecture Notes in Computer Science, Vol. 11320, pp. 299-311, 2018.
  doi: 10.1007/978-3-030-03991-2_29 | arXiv: 1806.09328

• A User-Friendly Hybrid Sparse Matrix Class in C++
  C. Sanderson, R. Curtin.
  Lecture Notes in Computer Science (LNCS), Vol. 10931, pp. 422-430, 2018.
  doi: 10.1007/978-3-319-96418-8_50 | arXiv: 1805.03380

• gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation
  C. Sanderson, R. Curtin.
  Journal of Open Source Software, Vol. 2, 2017.
  doi: 10.21105/joss.00365

• An Open Source C++ Implementation of Multi-Threaded Gaussian Mixture Models, k-Means and Expectation Maximisation
  C. Sanderson, R. Curtin.
  International Conference on Signal Processing and Communication Systems, 2017.
  doi: 10.1109/ICSPCS.2017.8270510 | arXiv: 1707.09094

• Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
  M. Harandi, R. Hartley, B. Lovell, C. Sanderson.
  IEEE Transactions on Neural Networks and Learning Systems, Vol. 27, No. 6, 2016.
  doi: 10.1109/TNNLS.2014.2387383

• Armadillo: a template-based C++ library for linear algebra
  C. Sanderson, R. Curtin.
  Journal of Open Source Software, Vol. 1, 2016.
  doi: 10.21105/joss.00026

• Exploiting Temporal Information for DCNN-based Fine-Grained Object Classification
  Z. Ge, C. McCool, C. Sanderson, P. Wang, L. Liu, I. Reid, P. Corke
  International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2016.
  doi: 10.1109/DICTA.2016.7797039 | arXiv: 1608.00486
  (see also the associated VB100 video dataset)

• Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks
  Z. Ge, A. Bewley, C. McCool, P. Corke, B. Upcroft, C. Sanderson.
  IEEE Winter Conference on the Applications of Computer Vision, 2016.
  doi: 10.1109/WACV.2016.7477700
  (see also the associated source code)

• Towards Miss Universe Automatic Prediction: The Evening Gown Competition
  J. Carvajal, A. Wiliem, C. Sanderson, B. Lovell.
  International Conference on Pattern Recognition, 2016.
  doi: 10.1109/ICPR.2016.7899781 | arXiv: 1604.07547
  (see also the associated Miss Universe dataset)

• Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions
  J. Carvajal, A. Wiliem, C. McCool, B.C. Lovell, C. Sanderson.
  Lecture Notes in Computer Science (LNCS), Vol. 9794, pp. 88-100, 2016.
  doi: 10.1007/978-3-319-42996-0_8 | arXiv: 1602.01599

• Joint Recognition and Segmentation of Actions via Probabilistic Integration of Spatio-Temporal Fisher Vectors
  J. Carvajal, C. McCool, B.C. Lovell, C. Sanderson.
  Lecture Notes in Computer Science (LNCS), Vol. 9794, pp. 115-127, 2016.
  doi: 10.1007/978-3-319-42996-0_10 | arXiv: 1602.01601

• Log-Euclidean Bag of Words for Human Action Recognition
  M. Faraki, M. Palhang, C. Sanderson.
  IET Computer Vision, Vol. 9, No. 3, 2015.
  doi: 10.1049/iet-cvi.2014.0018

• Extrinsic Methods for Coding and Dictionary Learning on Grassmann Manifolds
  M. Harandi, R. Hartley, C. Shen, B. Lovell, C. Sanderson.
  International Journal of Computer Vision, Vol. 114, No. 2, 2015.
  doi: 10.1007/s11263-015-0833-x

• Subset Feature Learning for Fine-Grained Category Classification
  Z. Ge, C. McCool, C. Sanderson, P. Corke.
  Computer Vision and Pattern Recognition (CVPR) Workshop on Deep Vision, 2015.
  doi: CVPRW.2015.7301271

• Modelling Local Deep Convolutional Neural Network Features to Improve Fine-Grained Image Classification
  Z. Ge, C. McCool, C. Sanderson, P. Corke.
  IEEE International Conference on Image Processing, 2015.
  doi: 10.1109/ICIP.2015.7351579

• Fine-Grained Bird Species Recognition via Hierarchical Subset Learning
  Z. Ge, C. McCool, C. Sanderson, A. Bewley, P. Corke.
  IEEE International Conference on Image Processing, 2015.
  doi: 10.1109/ICIP.2015.7350861

• Bags of Affine Subspaces for Robust Object Tracking
  S. Shirazi, C. Sanderson, C. McCool, M. Harandi.
  International Conference on Digital Image Computing: Techniques and Applications, 2015.
  doi: 10.1109/DICTA.2015.7371239

• Content Specific Feature Learning for Fine-Grained Plant Classification
  Z. Ge, C. McCool, C. Sanderson, P. Corke.
  Working Notes of CLEF 2015.

• Automatic Classification of Human Epithelial Type 2 Cell Indirect Immunofluorescence Images using Cell Pyramid Matching
  A. Wiliem, C. Sanderson, Y. Wong, P. Hobson, R. Minchin, B.C. Lovell.
  Pattern Recognition, Vol. 47, No. 7, 2014.
  doi: 10.1016/j.patcog.2013.10.014 | arXiv: 1403.3780

• On Robust Face Recognition via Sparse Coding: The Good, The Bad and The Ugly
  Y. Wong, M. Harandi, C. Sanderson.
  IET Biometrics, Vol. 3, No. 4, 2014.
  doi: 10.1049/iet-bmt.2013.0033 | arXiv: 1303.1624

• RcppArmadillo: Accelerating R with High-Performance C++ Linear Algebra
  D. Eddelbuettel, C. Sanderson.
  Computational Statistics and Data Analysis, Vol. 71, 2014.
  doi: 10.1016/j.csda.2013.02.005

• Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients
  J. Carvajal, C. Sanderson, C. McCool, B.C. Lovell
  Workshop on Machine Learning for Sensory Data Analysis (MLSDA), 2014.
  doi: 10.1145/2689746.2689748

• Object Tracking via Non-Euclidean Geometry: A Grassmann Approach
  S. Shirazi, M. Harandi, B.C. Lovell, C. Sanderson.
  IEEE Winter Conference on the Applications of Computer Vision, 2014.
  doi: 10.1109/WACV.2014.6836008
  (see also the associated slides)

• Random Projections on Manifolds of Symmetric Positive Definite Matrices for Image Classification
  A. Alavi, A. Wiliem, K. Zhao, B.C. Lovell, C. Sanderson.
  IEEE Winter Conference on the Applications of Computer Vision, 2014.
  doi: 10.1109/WACV.2014.6836085

• Summarisation of Short-Term and Long-Term Videos using Texture and Colour
  J. Carvajal, C. McCool, C. Sanderson.
  IEEE Winter Conference on the Applications of Computer Vision, 2014.
  doi: 10.1109/WACV.2014.6836025

• Matching Image Sets via Adaptive Multi Convex Hull
  S. Chen, A. Wiliem, C. Sanderson, B.C. Lovell.
  IEEE Winter Conference on the Applications of Computer Vision, 2014.
  doi: 10.1109/WACV.2014.6835985

• Kernel Analysis on Grassmann Manifolds for Action Recognition
  M. Harandi, C. Sanderson, S. Shirazi, B.C. Lovell.
  Pattern Recognition Letters, Vol. 34, No. 15, 2013.
  doi: 10.1016/j.patrec.2013.01.008

• Improved Foreground Detection via Block-based Classifier Cascade with Probabilistic Decision Integration
  V. Reddy, C. Sanderson, B.C. Lovell.
  IEEE Transactions on Circuits and Systems for Video Technology, Vol. 23, No. 1, 2013.
  tags: background subtraction, surveillance, object detection
  doi: 10.1109/TCSVT.2012.2203199

• Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
  M. Harandi, C. Sanderson, C. Shen, B.C. Lovell.
  IEEE Int. Conf. Computer Vision (ICCV), 2013.
  doi: 10.1109/ICCV.2013.387

• Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
  S. Chen, C. Sanderson, M. Harandi, B.C. Lovell.
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2013.
  doi: 10.1109/CVPR.2013.65

• Multi-Shot Person Re-Identification via Relational Stein Divergence
  A. Alavi, Y. Yang, M. Harandi, C. Sanderson.
  IEEE Int. Conf. Image Processing, 2013.
  doi: 10.1109/ICIP.2013.6738731
  (see also the associated slides)

• Spatio-Temporal Covariance Descriptors for Action and Gesture Recognition
  A. Sanin, C. Sanderson, M. Harandi, B.C. Lovell.
  Workshop on the Applications of Computer Vision, 2013.
  doi: 10.1109/WACV.2013.6475006

• Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
  A. Wiliem, Y. Wong, C. Sanderson, P. Hobson, S. Chen, B.C. Lovell.
  Workshop on the Applications of Computer Vision, 2013.
  doi: 10.1109/WACV.2013.6475005

• Relational Divergence Based Classification on Riemannian Manifolds
  A. Alavi, M. Harandi, C. Sanderson.
  Workshop on the Applications of Computer Vision, 2013.
  doi: 10.1109/WACV.2013.6475007

• Shadow Detection: A Survey and Comparative Evaluation of Recent Methods
  A. Sanin, C. Sanderson, B.C. Lovell.
  Pattern Recognition, Vol. 45, No. 4, 2012.
  doi: 10.1016/j.patcog.2011.10.001 | arXiv: 1304.1233

• Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
  M. Harandi, C. Sanderson, R. Hartley, B.C. Lovell.
  European Conference on Computer Vision (ECCV), 2012.
  doi: 10.1007/978-3-642-33709-3_16

• Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification
  C. Sanderson, M. Harandi, Y. Wong, B.C. Lovell.
  IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2012.
  doi: 10.1109/AVSS.2012.23

• Clustering on Grassmann Manifolds via Kernel Embedding with Application to Action Analysis
  S. Shirazi, M. Harandi, C. Sanderson, A. Alavi, B.C. Lovell.
  IEEE International Conference on Image Processing, 2012.
  doi: 10.1109/ICIP.2012.6466976

• K-Tangent Spaces on Riemannian Manifolds for Improved Pedestrian Detection
  A. Sanin, C. Sanderson, M. Harandi, B.C. Lovell.
  IEEE International Conference on Image Processing, 2012.
  doi: 10.1109/ICIP.2012.6466899

• On Robust Biometric Identity Verification via Sparse Encoding of Faces: Holistic vs Local Approaches
  Y. Wong, M. Harandi, C. Sanderson, B.C. Lovell.
  IEEE International Joint Conference on Neural Networks (IJCNN), 2012.
  (see also extended version)
  doi: 10.1109/IJCNN.2012.6252611

• Kernel Analysis over Riemannian Manifolds for Visual Recognition of Actions, Pedestrians and Textures
  M. Harandi, C. Sanderson, A. Wiliem, B.C. Lovell.
  IEEE Workshop on the Applications of Computer Vision, 2012.
  doi: 10.1109/WACV.2012.6163005

• A Low-Complexity Algorithm for Static Background Estimation from Cluttered Image Sequences in Surveillance Contexts
  V. Reddy, C. Sanderson, B.C. Lovell.
  Image and Video Processing, 2011.
  SpringerOpen: 10.1155/2011/164956 | arXiv: 1303.2465

• Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework
  S. Chen, S. Mau, M. Harandi, C. Sanderson, A. Bigdeli, B.C. Lovell.
  Image and Video Processing, 2011.
  SpringerOpen: 10.1155/2011/790598

• Graph Embedding Discriminant Analysis on Grassmannian Manifolds for Improved Image Set Matching
  M. Harandi, C. Sanderson, S. Shirazi, B.C. Lovell.
  IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2011.
  doi: 10.1109/CVPR.2011.5995564

• Improved Anomaly Detection in Crowded Scenes via Cell-based Analysis of Foreground Speed, Size and Texture
  V. Reddy, C. Sanderson, B.C. Lovell.
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2011.
  tags: anomaly detection, surveillance.
  doi: 10.1109/CVPRW.2011.5981799

• Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition
  Y. Wong, S. Chen, S. Mau, C. Sanderson, B.C. Lovell.
  IEEE Conf. Computer Vision and Pattern Recognition Workshops (CVPRW), 2011.
  tags: subset selection, image processing, surveillance.
  doi: 10.1109/CVPRW.2011.5981881

• Ensemble of Furthest Subspace Pairs for Enhanced Image Set Matching
  M. Harandi, C. Sanderson, A. Bigdeli, B.C. Lovell.
  IEEE International Conference on Image Processing, 2011.
  doi: 10.1109/ICIP.2011.6116683

• MRF-based Background Initialisation for Improved Foreground Detection in Cluttered Surveillance Videos
  V. Reddy, C. Sanderson, A. Sanin, B.C. Lovell.
  Lecture Notes in Computer Science (LNCS), Vol. 6494, pp. 547-559, 2011.
  tags: surveillance, background estimation, Markov random fields, region processing.
  doi: 10.1007/978-3-642-19318-7_43

• Adaptive Patch-Based Background Modelling for Improved Foreground Object Segmentation and Tracking
  V. Reddy, C. Sanderson, A. Sanin, B.C. Lovell.
  International Conference on Advanced Video and Signal Based Surveillance (AVSS), Boston, 2010.
  tags: surveillance, foreground estimation, region processing, tracking.
  doi: 10.1109/AVSS.2010.84

• Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios
  A. Sanin, C. Sanderson, B.C. Lovell.
  International Conference on Pattern Recognition, 2010.
  tags: surveillance, shadow removal, tracking.
  doi: 10.1109/ICPR.2010.43

• Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference
  Y. Wong, C. Sanderson, S. Mau, B.C. Lovell.
  International Conference on Pattern Recognition, 2010.
  tags: surveillance, identity inference, local features.
  doi: 10.1109/ICPR.2010.299

• Video Face Matching using Subset Selection and Clustering of Probabilistic Multi-Region Histograms
  S. Mau, S. Chen, C. Sanderson, B.C. Lovell.
  International Conference of Image and Vision Computing New Zealand (IVCNZ), Queenstown, 2010.
  tags: identity inference, local features, clustering, subset selection.
  doi: 10.1109/IVCNZ.2010.6148860

• Learning-based Face Synthesis for Pose-Robust Recognition from Single Image
  A. Asthana, C. Sanderson, T. Gedeon, R. Goecke.
  British Machine Vision Conference (BMVC), London, 2009.
  tags: identity inference, image synthesis.
  doi: 10.5244/C.23.31

• Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference
  C. Sanderson, B.C. Lovell.
  LNCS 5558, pp. 199-208, 2009.
  tags: surveillance, identity inference, local features, bag of words.
  doi: 10.1007/978-3-642-01793-3_21

• Regression Based Non-Frontal Face Synthesis for Improved Identity Verification
  Y. Wong, C. Sanderson, B.C. Lovell.
  International Conference on Computer Analysis of Images and Patterns (CAIP), Germany, 2009.
  tags: image synthesis, local representation.
  doi: 10.1007/978-3-642-03767-2_14

• An Efficient and Robust Sequential Algorithm for Background Estimation in Video Surveillance
  V. Reddy, C. Sanderson, B.C. Lovell.
  IEEE International Conference on Image Processing (ICIP), 2009.
  doi: 10.1109/ICIP.2009.5413450

• An Efficient Background Estimation Algorithm for Embedded Smart Cameras
  V. Reddy, C. Sanderson, B.C. Lovell, A. Bigdeli.
  ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC), 2009.
  doi: 10.1109/ICDSC.2009.5289348

• On Statistical Approaches to Target Silhouette Classification in Difficult Conditions
  C. Sanderson, D. Gibbins, S. Searle.
  Digital Signal Processing, Vol. 18, No. 3, 2008.
  tags: target recognition, local features, bag of words.
  doi: 10.1016/j.dsp.2007.05.008

• On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security
  B.C. Lovell, S. Chen, A. Bigdeli, E. Berglund, C. Sanderson.
  IEEE Int. Conf. Control, Automation, Robotics and Vision (ICARCV), 2008.
  doi: 10.1109/ICARCV.2008.4795605

• Experimental Analysis of Face Recognition on Still and CCTV images
  S. Chen, E. Berglund, A. Bigdeli, C. Sanderson, B.C. Lovell.
  IEEE Conf. Advanced Video and Signal based Surveillance, 2008.
  doi: 10.1109/AVSS.2008.15

• Representative Feature Chain for Single Gallery Image Face Recognition
  S. Chen, C. Sanderson, S. Sun and B.C. Lovell.
  International Conference on Pattern Recognition, 2008.
  doi: 10.1109/ICPR.2008.4760975

• Towards Pose-Invariant 2D Face Classification for Surveillance
  C. Sanderson, T. Shan, B.C. Lovell.
  Analysis and Modeling of Faces and Gestures (AMFG), LNCS, Vol. 4778, 2007, pp. 276-289.
  (held in conjunction with the International Conference on Computer Vision (ICCV), Rio de Janeiro, 2007)
  tags: surveillance, image synthesis, local features, shape representation, active shape models.
  doi: 10.1007/978-3-540-75690-3_21

• Classifying and Tracking Multiple Persons for Proactive Surveillance of Mass Transport Systems
  S. Kong, C. Sanderson, B.C. Lovell.
  IEEE Conf. Advanced Video and Signal based Surveillance (AVSS), London, 2007.
  doi: 10.1109/AVSS.2007.4425303

• Towards Robust Face Recognition for Intelligent-CCTV Based Surveillance Using One Gallery Image
  T. Shan, S. Chen, C. Sanderson, B.C. Lovell.
  IEEE Conf. Advanced Video and Signal based Surveillance (AVSS), London, 2007.
  doi: 10.1109/AVSS.2007.4425356

• On Transforming Statistical Models for Non-Frontal Face Verification
  C. Sanderson, S. Bengio, Y. Gao.
  Pattern Recognition, Vol. 39, No. 2, 2006.
  tags: model synthesis, identity inference, holistic features, local features, bag of words, pose variations.
  doi: 10.1016/j.patcog.2005.07.001

• User Authentication via Adapted Statistical Models of Face Images
  F. Cardinaux, C. Sanderson, S. Bengio.
  IEEE Transactions on Signal Processing, Vol. 54, No. 1, 2006.
  tags: security, identity inference, local features, bag of words, hidden Markov models.
  doi: 10.1109/TSP.2005.861075

• Short Text Authorship Attribution via Sequence Kernels, Markov Chains and Author Unmasking: An Investigation
  C. Sanderson, S. Guenter.
  International Conference on Empirical Methods in Natural Language Processing (EMNLP), Sydney, 2006.
  tags: kernels, statistical modelling, text processing, identity inference.
  doi: 10.3115/1610075.1610142 | ACL Anthology Archive

• On Authorship Attribution via Markov Chains and Sequence Kernels
  C. Sanderson, S. Guenter.
  International Conference on Pattern Recognition, Hong Kong, 2006.
  tags: kernels, statistical modelling, text processing, identity inference.
  doi: 10.1109/ICPR.2006.899
  (see also the extended EMNLP paper above)

• A Simple Formula for the High Regularisation Limit of SVMs & Ridge Regression with Application to Gene Selection and Classification of Microarrays
  J. Bedo, T. Caetano, B. Parker, C. Sanderson, P. Sunehag, A. Kowalczyk.
  Poster, presented at the 14th Annual Conference on Intelligent Systems for Molecular Biology (ISMB), Fortaleza, Brazil, 2006.

• An Efficient Alternative to SVM Based Recursive Feature Elimination with Applications in Natural Language Processing and Bioinformatics
  J. Bedo, C. Sanderson, A. Kowalczyk.
  AI 2006: Advances in Artificial Intelligence, LNCS, Volume 4304/2006, Springer Berlin / Heidelberg.
  tags: kernels, SVM, microarrays, text processing.
  doi: 10.1007/11941439_21

• On Local Features for GMM Based Face Verification
  C. Sanderson, M. Saban, Y. Gao.
  International Conference on Information Technology and Applic., Sydney, 2005.
  doi: 10.1109/ICITA.2005.194

• Identity Verification Using Speech and Face Information
  C. Sanderson, K. K. Paliwal.
  Digital Signal Processing, Vol. 14, No. 5, 2004.
  (this article received a most cited paper award)
  tags: identity inference, speech processing, face processing, information fusion.
  doi: 10.1016/j.dsp.2004.05.001

• Statistical Transformations of Frontal Models for Non-Frontal Face Verification
  C. Sanderson, S. Bengio.
  IEEE International Conference on Image Processing Singapore, 2004.
  tags: model synthesis, identity inference, local features, bag of words, pose variations.
  doi: 10.1109/ICIP.2004.1418822

• Spectral Subband Centroids as Complementary Features for Speaker Authentication
  N. Poh, C. Sanderson, S. Bengio.
  International Conference on Biometric Authentication (ICBA), Hong Kong, 2004.
  tags: identity inference, speech processing, feature extraction.
  doi: 10.1007/978-3-540-25948-0_86
  

• Face Verification Using Adapted Generative Models
  F. Cardinaux, C. Sanderson, S. Bengio.
  IEEE International Conference on Face and Gesture Recognition (AFGR), Seoul, 2004.
  tags: security, identity inference, local features, bag of words, hidden Markov models.
  doi: 10.1109/AFGR.2004.1301636

• Face Authentication Competition on the BANCA Database
  K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, A. Kostyn, S. Marcel, S. Bengio, F. Cardinaux, C. Sanderson, N. Poh, Y. Rodriguez, K. Kryszczuk, J. Czyz, L. Vandendorpe, J. Ng, H. Cheung, B. Tang.
  International Conference on Biometric Authentication (ICBA), Hong Kong, 2004.
  doi: 10.1007/978-3-540-25948-0_2

• Extrapolating Single View Face Models for Multi-View Recognition
  C. Sanderson, S. Bengio.
  Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Melbourne, 2004.
  doi: 10.1109/ISSNIP.2004.1417526

• On Classifying Silhouettes in Adverse Conditions
  C. Sanderson, D. Gibbins.
  Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Melbourne, 2004.
  tags: target recognition, local features.
  doi: 10.1109/ISSNIP.2004.1417457
  (see also the extended article)

• Noise compensation in a person verification system using face and multiple speech features
  C. Sanderson, K. K. Paliwal.
  Pattern Recognition, Vol. 36, No. 2, 2003.
  doi: 10.1016/S0031-3203(02)00031-6

• Structurally Noise Resistant Classifier for Multi-Modal Person Verification
  C. Sanderson, K. K. Paliwal.
  Pattern Recognition Letters, Vol. 24, No. 16, 2003.
  doi: 10.1016/S0167-8655(03)00168-5

• Fast Features for Face Authentication Under Illumination Direction Changes
  C. Sanderson, K. K. Paliwal.
  Pattern Recognition Letters, Vol. 24, No. 14, 2003.
  doi: 10.1016/S0167-8655(03)00070-9

• Augmenting Frontal Face Models for Non-Frontal Verification
  C. Sanderson, S. Bengio.
  Workshop on Multi-Modal User Authentication (MMUA), Santa Barbara, 2003.
  [proceedings]

• Robust Features for Frontal Face Authentication in Difficult Image Conditions
  C. Sanderson, S. Bengio.
  Lecture Notes in Computer Science, Vol. 2688, 2003.
  tags: security, face processing, local features, feature extraction.
  doi: 10.1007/3-540-44887-X_59

• Comparison of MLP and GMM Classifiers for Face Verification on XM2VTS
  F. Cardinaux, C. Sanderson, S. Marcel.
  Lecture Notes in Computer Science, Vol. 2688, 2003.
  tags: security, face processing, neural networks, statistical classifiers.
  doi: 10.1007/3-540-44887-X_106

• Face Verification Competition on the XM2VTS Database
  K. Messer, J. Kittler, M. Sadeghi, S. Marcel, C. Marcel, S. Bengio, F. Cardinaux, C. Sanderson, J. Czyz, L. Vandendorpe, S. Srisuk, M. Petrou, W. Kurutach, A. Kadyrov, R. Paredes, B. Kepenekci, F. B. Tek, G. B. Akar, F. Deravi, N. Mavity.
  Lecture Notes in Computer Science, Vol. 2688, 2003.
  doi: 10.1007/3-540-44887-X_112

• Speech & Face Based Biometric Authentication at IDIAP
  C. Sanderson, S. Bengio, H. Bourlard, J. Mariethoz, R. Collobert, M. F. BenZeghiba, F. Cardinaux, S. Marcel.
  IEEE International Conference on Multimedia & Expo (ICME), Baltimore, 2003.
  tags: speech processing, face processing, information fusion.
  doi: 10.1109/ICME.2003.1221233

• Noise Resistant Audio-Visual Verification via Structural Constraints
  C. Sanderson, K. K. Paliwal.
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong, 2003.
  doi: 10.1109/ICASSP.2003.1200071

• Polynomial Features for Robust Face Authentication
  C. Sanderson, K. K. Paliwal.
  IEEE International Conference on Image Processing (ICIP), Rochester, 2002.
  doi: 10.1109/ICIP.2002.1039143

• Likelihood Normalization for Face Authentication in Variable Recording Conditions
  C. Sanderson, K. K. Paliwal.
  IEEE International Conference on Image Processing (ICIP), Rochester, 2002.
  doi: 10.1109/ICIP.2002.1038019

• Noise Compensation in a Multi-Modal Verification System
  C. Sanderson, K. K. Paliwal.
  IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Salt Lake City, 2001.
  doi: 10.1109/ICASSP.2001.940791

• Information Fusion for Robust Speaker Verification
  C. Sanderson, K. K. Paliwal.
  European Conference on Speech Communication and Technology (Eurospeech), 2001.

• Adaptive Multi-Modal Person Verification System
  C. Sanderson, K. K. Paliwal.
  IEEE Pacific-Rim Conference on Multimedia (IEEE-PCM), Sydney, 2000.

• Multi-Modal Person Verification System Based on Face Profiles and Speech
  C. Sanderson, K. K. Paliwal.
  International Symposium on Signal Processing and its Applications (ISSPA), Brisbane, 1999.
  doi: 10.1109/ISSPA.1999.815828

• Effect of Different Sampling Rates and Feature Vector Sizes on Speech Recognition Performance
  C. Sanderson, K. K. Paliwal.
  IEEE Region 10 Annual Conference (TENCON), Brisbane, 1997.
  doi: 10.1109/TENCON.1997.647282

 
 
Technical Reports, Presentation Slides, Patents

• What is a GPU? An expert explains the chips powering the AI boom
  C. Sanderson.
  The Conversation, 6 March 2024.
  CSIRO news | theconversation.com | techxplore.com

• PyArmadillo: an alternative approach to linear algebra in Python
  Jason Rumengan, Terry Yue Zhuo, Conrad Sanderson.
  Technical Report, Data61/CSIRO, 2021.

• Armadillo: C++ Template Metaprogramming for Compile-Time Optimization of Linear Algebra
  C. Sanderson, R. Curtin.
  Platform for Advanced Scientific Computing (PASC) Conference, Switzerland, 2017.

• Identifying Matching Images
  C. Sanderson, Y. Wong.
  US Patent 9165184B2, 2015.
  [patent text]
  

• Computer Vision and Image Processing for Automated Surveillance
  C. Sanderson.
  IEEE SigPort, 2015.

• Object Tracking and Person Re-Identification on Manifolds
  Tutorial on Riemannian Geometry in Computer Vision.
  Asian Conference on Computer Vision (ACCV), 2014.
  

• Armadillo: An Open Source C++ Linear Algebra Library for Fast Prototyping and Computationally Intensive Experiments
  C. Sanderson.
  Technical Report, NICTA, 2010.
  tags: software, linear algebra, matrix library, C++ templates.
  

• Kernel Techniques for Pattern Analysis
  C. Sanderson.
  Lecture slides for UQ ELEC7005 course, 2009.

• Advanced C++ Template Techniques
  C. Sanderson.
  Lecture slides for UQ COMP7305 course, 2009.

• On Local Feature Approaches to Classifying Silhouettes in IR Images
  C. Sanderson, D. Gibbins.
  CSSIP Commercial Report 34-05, Adelaide, Australia, 2005.

• Silhouette Classification in Adverse Conditions
  C. Sanderson, D. Gibbins.
  CSSIP Commercial Report 11-04, Adelaide, Australia, 2004.

• Information Fusion and Person Verification Using Speech & Face Information
  C. Sanderson, K. Paliwal
  IDIAP Research Report 02-33, Switzerland, 2002.

• Japanese Phoneme Recognition Experiments on ATR's Travel Task (SDB) using HTK v2.02
  C. Sanderson, H. Singer.
  Technical Report TR-IT-0223, ATR Interpreting Telecommunications Research Laboratories, Kyoto, Japan, 1997.

 
 
Theses

• Action Analysis and Video Summarisation to Efficiently Manage and Interpret Video Data
  J. Carvajal.
  PhD Thesis, University of Queensland, Australia, 2016.
  

• Robust Fine-Grained Image Classification
  Z-Y. Ge.
  PhD Thesis, Queensland University of Technology, Australia, 2016.
  tags: video analysis, deep learning

• Image Analysis on Symmetric Positive Definite Manifolds
  A. Alavi.
  PhD Thesis, University of Queensland, Australia, 2014.
  tags: video analysis, Riemannian manifolds

• Improving Representation and Classification of Image and Video Data for Surveillance Applications
  A. Sanin.
  PhD Thesis, University of Queensland, Australia, 2013.
  tags: shadow removal, action recognition, Riemannian manifolds

• Towards Robust Identity Inference Under Suverillance Environments: From Still Images to Video Sequences
  Y. Wong.
  PhD Thesis, University of Queensland, Australia, 2012.
  tags: patch analysis, face recognition, sparse coding, surveillance

• Contextual Spatial Analysis and Processing for Visual Surveillance Applications
  V. Reddy.
  PhD Thesis, University of Queensland, Australia, 2012.
  tags: patch analysis, foreground detection, background estimation, background subtraction, anomaly detection.

• Robust Person and Vehicle Tracking for Intelligent Visual Surveillance
  Suyu Kong.
  MPhil Thesis, University of Queensland, Australia, 2008.
  

• Automatic Person Verification Using Speech and Face Information
  C. Sanderson.
  PhD Thesis, Griffith University, Queensland, Australia, 2003.
  tags: security, biometrics, face processing, speech processing, information fusion.

 
 
Books & Chapters

• Intelligent Surveillance and Pose-Invariant 2D Face Classification
  B.C. Lovell, C. Sanderson, T. Shan.
  In: Machine Interpretation of Patterns: Image Analysis and Data Mining (editors: R.K. De, D.P. Mandal, A. Ghosh).
  Statistical Science and Interdisciplinary Research, Vol. 11, World Scientific, 2010.
  doi: 10.1142/9789814299190_0011
  

• Intelligent CCTV for Mass Transport Security: Challenges and Opportunities for Video and Face Processing
  C. Sanderson, A. Bigdeli, T. Shan, S. Chen, E. Berglund, B.C. Lovell.
  In: Progress In Computer Vision And Image Analysis (editors: H. Bunke, J. Villanueva, G. Sánchez, X. Otazu).
  Series in Machine Perception and Artificial Intelligence, Vol. 73, World Scientific, 2009.
  doi: 10.1142/9789812834461_0030
  

• Biometric Person Recognition: Face, Speech and Fusion
  C. Sanderson.
  VDM-Verlag, 2008.
  ISBN 978-3-639-02769-3.
  tags: book, security, biometrics, face processing, speech processing, information fusion.
  external links: Amazon | Book Depository | UQ eSpace