|
Mehrtash Harandi
B.Sc, M.Sc, PhD
|
|
Postdoctoral Research Fellow
Queensland Research Laboratory (QRL)
National ICT Australia (NICTA)
Brisbane QLD 4067 Australia
|
|
Adjunct Lecturer
School of ITEE
University of Queensland (UQ)
Brisbane QLD 4067 Australia
|
|
Office: +61 7 3300 8673
Fax: +61 7 3300 8420
Email: mehrtash dot harandi at nicta dot com dot au
|
|
|
I am a research fellow at NICTA, and an adjunct lecturer at the University of Queensland
, working with
Prof. Richard Hartley,
Prof. Brian Lovell
and
Dr. Conrad Sanderson.
More Info:
LinkedIn
|
Teaching:
Signal and Image processing II, The University of Queensland, 2010, 2011,
2012
Basic Mathematics for Neuroscientists, Institute for Research in Fundamental Science, 2006, 2007
|
Students and Colleagues:
I am cosupervising the following PhD student(s):
Sareh Shirazi
, Inference on Grassmann Manifolds, Co-supervised with Prof. Brian Lovell
I am collaborating with the following research fellows and PhD students:
Ehsan Norouznezhad
, Video Analysis using Spatio-Temporal Filters
Yongkang Wong
, Face Recognition
Mahsa Baktash, Subspace Analysis for Video Retrieval
Dr. Shaokang Chen
, Manifold Analysis for Visual Recognition
Dr. Arnold Williem, Manifold Analysis for Visual Recognition
|
Selected Publications:
-
Clustering on Grassmann Manifolds via Kernel Embedding with Application to Action Analysis
S. Shirazi, M. Harandi, C. Sanderson, B.C. Lovell.
IEEE Conf. Image Processing (ICIP), 2012.
-
Kernel Analysis over Riemannian Manifolds for Visual Recognition of Actions, Pedestrians and Textures
M. Harandi, C. Sanderson, A. Wiliem and B.C. Lovell.
IEEE Workshop on the Applications of Computer Vision (WACV), Colorado, 2012.
-
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.
-
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition
M. Harandi, M. Nili Ahmadabadi and B.N. Araabi.
International Journal of Computer Vision (IJCV), vol. 81, no. 2, pp. 191-204, 2009.
|