Anders Eriksson
ARC Future Fellow
VC Research Fellow
ARC DECRA Fellow
School of Electrical Engineering and Computer Science
Queensland University of Technology
Office: G423-06, Phone: (+61) (07) 3138 5187, Email:

About | Prospective PhD Students | Teaching | Research Grants | Publications | Bio

Motion Deblurring for Light Fields,
D. Dansereau, A. Eriksson and Jurgen Leitner,
2nd Workshop on Light Fields for Computer Vision (LF4CV) at CVPR 2017.
A Consensus-based Approach to Distributed Large-Scale Bundle Adjustment,
A. Eriksson, Bastian J., M. Isaksson and T. Chin,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Guaranteed Outlier Removal with Mixed Integer Linear Programs,
T. Chin, Y. Kee, A. Eriksson and F. Neumann,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Fast Rotation Search with Stereographic Projections for 3D Registration,
A. P. Bustos, T. J. Chin, A. Eriksson and H. Li,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016.
The k-Support Norm and Convex Envelopes of Cardinality and Rank,
A. Eriksson, T Pham, T-J. Chin and I. Reid,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
The k-Support Norm and Convex Envelopes of Cardinality and Rank,
A. Eriksson, T Pham, T-J. Chin and I. Reid,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
Efficient Globally Optimal Consensus Maximisation with Tree Search,
T-J. Chin, P. Purkait, A. Eriksson and D. Suter,
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [Best Paper Honorable Mention]

 

News

  • ARC Future Fellowship 2017-2021
  • CVPR 2017 Workshop Paper Accepted
  • ARC Discovery Project 2017-2020
  • ECCV 2016 Outstanding Reviewer Award
  • 2 CVPR 2016 papers accepted
  • Best Paper Runner Up - CVPR 2015.


About

I am an ARC Future Fellow and former ARC DECRA and Vice Chancellor's Research Fellow at the School of Electrical Engineering and Computer Science at Queensland University of Technology and a member of the Robotics and Autonomous Systems discipline and the Australian Centre for Robotic Vision. My research area include optimization theory and numerical methods applied to the fields of Computer Vision and Machine Learning.



Prospective PhD Students

I am actively recruiting PhD candidates. I currently have a fully funded PhD position in optimization and computer vision starting in 2017.

There are a number of scholarships offered by Queensland University of Technology and the Australian Government. These are available to both domestic and international students. More information can be found here.

If you are interested in any of these opportunities then send me an email with your CV, your research interests and any publications you might have.



Teaching

2014

COMP SCI 3420/4402 Introduction to Geometric Algorithms
COMP SCI 3007/7059 Artificial Intelligence

2013

COMP SCI 4022/7022 Computer Vision
COMP SCI 4401 Introduction to Statistical Machine Learning
COMP SCI 1102 Object Oriented Programming

2012

COMP SCI 4022/7022 Computer Vision

2011

COMP SCI 4022/7022 Computer Vision



Research Grants

  • ARC Future Fellow
    Australian Research Council
    The Role of Strong Duality in Computer Vision
    Sole Chief Investigator
    Project duration: 2017-2021
    Total budget: $808,140

  • ARC Discovery Project
    Australian Research Council
    One shot three-dimensional reconstruction of human anatomy and motion
    Chief Investigator
    Project duration: 2017-2020
    Total budget: $410,500

  • Vice-Chancellor's Research Fellowship
    Queensland University of Technology
    Sole Chief Investigator
    Project duration: 2016-2019

  • ARC Discovery Early Career Researcher Award - DECRA
    Australian Research Council
    Distributed Large-Scale Optimization Methods in Computer Vision
    Sole Chief Investigator
    Project duration: 2013-2016
    Total budget: $375,000

  • Data to Decisions
    Cooperative Research Centre
    Chief Investigator
    Project duration: 2014-2019
    Total budget: $25,000,000

  • Centre of Excellence in Robotic Vision
    Australian Research Council
    Associate Investigator
    Project duration: 2014-2021
    Total budget: $19,000,000

  • ARC Linkage Project
    Australian Research Council
    Semantic Change Detection Through Large-Scale Learning
    Chief Investigator
    Project duration: 2013-2015
    Total budget: $485,000


Publications

2017
Motion Deblurring for Light Fields,
D. G. Dansereau, A. Eriksson and J. Leitner,
2nd Workshop on Light Fields for Computer Vision (LF4CV) at CVPR 2017. [bibtex] [pdf]
Evaluation of Keypoint Detectors and Descriptors in Arthroscopic Images for Feature-based Matching Applications,
A. Marmol et al.,
Journal of Mechanical Design, June 2017. [bibtex] [pdf]
Novel Fault-Tolerance Indices for Redundantly Actuated Parallel Robots,
M. Isaksson, K. Marlow, A. Maciejewski and A. Eriksson,
Journal of Mechanical Design, no. 139, 2017. [bibtex] [pdf]
2016
A Comparison of the Yaw Constraining Performance of SCARA-Tau Parallel Manipulator Variants Via Screw Theory,
M. Isaksson, K. Marlow, T. Brogardh and A. Eriksson,
Robotics and Automation (ICRA), 2016 IEEE International Conference on. [bibtex] [pdf]
Fast Rotation Search with Stereographic Projections for 3D Registration,
A. P. Bustos, T. J. Chin, A. Eriksson and H. Li,
IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 99, 2016. [bibtex] [pdf]
A Consensus-based Approach to Distributed Large-Scale Bundle Adjustment,
A. Eriksson, J. Bastian, M. Isaksson and T. Chin,
Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on. [bibtex] [pdf]
Guaranteed Outlier Removal with Mixed Integer Linear Programs,
T. Chin, Y. Kee, A. Eriksson and F. Neumann,
Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on. [bibtex]
2015
The k-Support Norm and Convex Envelopes of Cardinality and Rank,
A. Eriksson, T. Thanh Pham, T. Chin and I. Reid,
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [bibtex] [pdf]
Efficient Globally Optimal Consensus Maximisation With Tree Search,
T. Chin, P. Purkait, A. Eriksson and D. Suter,
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015,
[Best Paper Honorable Mention]. [bibtex] [pdf]
High Breakdown Bundle Adjustment,
A. Eriksson, M. Isaksson and T. Chin,
Applications of Computer Vision (WACV), 2015 IEEE Winter Conference on, pp. 310-317. [bibtex] [pdf]
General, Nested, and Constrained Wiberg Minimization,
D. Strelow, Q. Wang, L. Si and A. Eriksson,
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PP, no. 99, 2015, pp. 1-1. [bibtex] [pdf]
A method for extending planar axis-symmetric parallel manipulators to spatial mechanisms,
M. Isaksson, A. Eriksson, M. Watson and T. Brogårdh,
Mechanism and Machine Theory, vol. 83, 2015, pp. 1 - 13. [bibtex] [pdf]
A Consensus-based Approach to Distributed Large-Scale Bundle Adjustment,
A. Eriksson, J. Bastian, M. Isaksson and T. Chin,
Technical Report, 2015. [bibtex] [pdf]
2014
Local Refinement for Stereo Regularization,
C. Olsson, J. Ulen and A. Eriksson,
Pattern Recognition (ICPR), 2014 22nd International Conference on, pp. 4056-4061. [bibtex] [pdf]
Pseudoconvex Proximal Splitting for L-infinity Problems in Multiview Geometry,
A. Eriksson and M. Isaksson,
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pp. 4066-4073. [bibtex] [pdf]
Analysis of the inverse kinematics problem for 3-DOF axis-symmetric parallel manipulators with parasitic motion,
M. Isaksson, A. Eriksson and S. Nahavandi,
Robotics and Automation (ICRA), 2014 IEEE International Conference on, pp. 5736-5743. [bibtex]
An Adversarial Optimization Approach to Efficient Outlier Removal,
J. Yu, A. Eriksson, T. Chin and D. Suter,
Journal of Mathematical Imaging and Vision, vol. 48, no. 3, 2014, pp. 451-466. [bibtex] [pdf]
Sampson distance based joint estimation of multiple homographies with uncalibrated cameras,
Z. L. Szpak, W. Chojnacki, A. Eriksson and A. v. d. Hengel,
Computer Vision and Image Understanding, vol. 125, 2014, pp. 200 - 213. [bibtex] [pdf]
2013
Fast convolutional sparse coding,
H. Bristow, A. Eriksson and S. Lucey,
Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pp. 391-398. [bibtex] [pdf]
2012
Efficient Computation of Robust Weighted Low-Rank Matrix Approximations Using the L_1 Norm,
A. Eriksson and A. Van den Hengel,
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, 2012, pp. 1681-1690. [bibtex] [pdf]
2011
Is face recognition really a Compressive Sensing problem?,
Q. Shi, A. Eriksson, A. van den Hengel and C. Shen,
Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 553-560. [bibtex] [pdf]
An adversarial optimization approach to efficient outlier removal,
J. Yu, A. Eriksson, T. Chin and D. Suter,
Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 399-406. [bibtex] [pdf]
Triangulating a Plane,
C. Olsson and A. Eriksson,
Scandanavian Conferences on Image Analysis (SCIA), 2011. [bibtex] [pdf]
Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints,
A. Eriksson, C. Olsson and F. Kahl,
Journal of Mathematical Imaging and Vision, vol. 39, no. 1, 2011, pp. 45-61. [bibtex] [pdf]
2010
Outlier Removal Using Duality,
C. Olsson, A. Eriksson and R. Hartley,
Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. [bibtex] [pdf]
Efficient Computation of Robust Low-Rank Matrix Approximations in the Presence of Missing Data using the L1 Norm,
A. Eriksson and A. v. d. Hengel,
Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010,
[Best Paper Award]. [bibtex] [pdf]
2009
Optimization on the Manifold of Multiple Homographies,
A. Eriksson and A. v. d. Hengel,
2nd IEEE International Workshop on Subspace Methods at the IEEE International Conference on Computer Vision, 2009. [bibtex] [pdf]
2008
Improved spectral relaxation methods for binary quadratic optimization problems,
C. Olsson, A. Eriksson and F. Kahl,
Computer Vision and Image Understanding, vol. 112, no. 1, 2008, pp. 3-13. [bibtex] [pdf]
Solving quadratically constrained geometrical problems using lagrangian duality,
C. Olsson and A. Eriksson,
International Conference on Pattern Recognition (ICPR), 2008. [bibtex] [pdf]
Optimization Methods for Large Scale Combinatorial Problems and Bijectivity Constrained Image Deformations,
A. Eriksson,
Ph.D. Thesis, Lund University, Faculty of Engineering, Centre for Mathematical Sciences, Mathematics, 2008. [bibtex] [pdf]
2007
Efficient Optimization for L-infinity problems using Pseudoconvexity,
C. Olsson, A. Eriksson and F. Kahl,
Proc. International Conference on Computer Vision (ICCV), 2007. [bibtex] [pdf]
Solving Large Scale Binary Quadratic Problems: Spectral Methods vs. Semidefinite Programming,
C. Olsson, A. Eriksson and F. Kahl,
Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007. [bibtex]
Segmenting with context,
A. Eriksson, C. Olsson and F. Kahl,
Proceedings of SCIA, 2007. [bibtex]
Normalized Cuts Revisited: A Reformulation for Segmentation with Linear Grouping Constraints,
A. Eriksson, C. Olsson and F. Kahl,
Proc. International Conference on Computer Vision (ICCV), 2007. [bibtex] [pdf]
Efficiently Solving the Fractional Trust Region Problem,
A. Eriksson, C. Olsson and F. Kahl,
Proc. Asian Conference on Computer Vision, 2007, pp. 796-805. [bibtex] [pdf]
2006
Image registration using thin plate splines,
A. Eriksson and K. Astrom,
International Conference on Pattern Recognition (ICPR'06). [bibtex]
Image segmentation using minimal graph cuts,
A. Eriksson, O. Barr and K. Astrom,
SSBA Symposium on Image Analysis, 2006. [bibtex]
2005
Bijective Thin-Plane Spline Mappings with Applications in Computer Vision,
A. Eriksson,
Licentiate Thesis, Lund University, Faculty of Engineering, Centre for Mathematical Sciences, Mathematics, 2005. [bibtex]
On the bijectivity of Thin-Plate Splines,
A. Eriksson and K. Astrom,
SSBA Symposium on Image Analysis, 2005. [bibtex]
2004
Learning in visual attention,
C. Balkenius, K. Astrom and A. Eriksson, 2004. [bibtex]
Robustness and Specificity in Object Detection,
A. Eriksson and K. Astrom,
International Conference on Pattern Recognition, vol. 3, 2004, pp. 87-90. [bibtex]


Bio

Dr Eriksson is an Australian Research Council Future Fellow and a Senior Research Associate at the School of Electrical Engineering and Computer Science, Queensland University of Technology. He received his Masters of Science degree in Electrical Engineering in 2000 and his PhD in Mathematics in 2008 from Lund University, Sweden. His research areas include optimisation theory and numerical methods applied to the fields of computer vision and machine learning. In 2010 his work on robust low-rank matrix approximation won the best paper award at the 23rd IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, USA.





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