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ORCIDMichael Antolovich
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2010 – 2019
- 2019
[j4]Peitao Wang
, Zhaoshui He, Kan Xie, Junbin Gao, Michael Antolovich, Beihai Tan:
A hybrid algorithm for low-rank approximation of nonnegative matrix factorization. Neurocomputing 364: 129-137 (2019)- [j3]Muhammad Ali, Junbin Gao, Michael Antolovich:
Parametric Classification of Bingham Distributions Based on Grassmann Manifolds. IEEE Trans. Image Process. 28(12): 5771-5784 (2019) - [j2]Fujiao Ju, Yanfeng Sun, Junbin Gao, Michael Antolovich, Jun-Liang Dong, Baocai Yin:
Tensorizing Restricted Boltzmann Machine. ACM Trans. Knowl. Discov. Data 13(3): 30:1-30:16 (2019) - [c17]Muhammad Rafiqul Islam, Manoranjan Paul, Michael Antolovich, Ashad Kabir:
Sports Highlights Generation using Decomposed Audio Information. ICME Workshops 2019: 579-584 - [c16]Muhammad Rafiqul Islam, Manoranjan Paul, Michael Antolovich:
Rain Streak Removal with Well-Recovered Moving Objects from Video Sequences Using Photometric Correlation. PSIVT Workshops 2019: 3-13 - 2017
- [c15]Peitao Wang, Zhaoshui He, Kan Xie, Junbin Gao, Michael Antolovich:
A Nonnegative Projection Based Algorithm for Low-Rank Nonnegative Matrix Approximation. ICONIP (1) 2017: 240-247 - 2016
- [c14]Muhammad Ali, Michael Antolovich, Boyue Wang:
Density estimation on Stiefel manifolds using matrix-Fisher model. CISP-BMEI 2016: 66-71 - [c13]Muhammad Ali, Michael Antolovich, Boyue Wang:
Recognition on Grassmann manifolds via parametric modelling. CISP-BMEI 2016: 2014-2019 - [c12]Muhammad Ali, Junbin Gao, Michael Antolovich:
MLE-Based Learning on Grassmann Manifolds. DICTA 2016: 1-7 - [c11]Muhammad Ali, Junbin Gao, Michael Antolovich:
Classification on Stiefel and Grassmann manifolds via maximum Likelihood estimation of matrix distributions. IJCNN 2016: 3751-3757 - 2015
- [c10]Fenglu Ge, Wayne Moore, Michael Antolovich:
Learning from Demonstration Using GMM, CHMM and DHMM: A Comparison. Australasian Conference on Artificial Intelligence 2015: 204-217 - 2014
- [c9]Geoff Bull, Junbin Gao, Michael Antolovich:
Image Segmentation Using Dictionary Learning and Compressed Random Features. DICTA 2014: 1-8 - [c8]Fenglu Ge, Wayne Moore, Michael Antolovich:
Convergence Problem in GMM Related Robot Learning from Demonstration. MIKE 2014: 62-71 - [c7]Geoff Bull, Junbin Gao, Michael Antolovich:
Rock Fragment Boundary Detection Using Compressed Random Features. VISIGRAPP (Selected Papers) 2014: 273-286 - [c6]Geoff Bull, Junbin Gao, Michael Antolovich:
Delineation of Rock Fragments by Classification of Image Patches using Compressed Random Features. VISAPP (1) 2014: 394-401 - 2013
- [j1]Daming Shi, Junbin Gao, Payam S. Rahmdel, Michael Antolovich, Tony Clark:
UND: Unite-and-Divide Method in Fourier and Radon Domains for Line Segment Detection. IEEE Trans. Image Process. 22(6): 2500-2505 (2013) - [c5]Vaenthan Thiruvarudchelvan, Wayne Moore, Michael Antolovich:
Improving the Efficiency of Spiking Network Learning. ICONIP (2) 2013: 172-179 - 2012
- [c4]Manoranjan Paul, Junbin Gao, Michael Antolovich:
3D motion estimation for 3D video coding. ICASSP 2012: 1189-1192 - 2011
- [c3]Fenglu Ge, Wayne Moore, Michael Antolovich, Junbin Gao:
Application of Learning from Demonstration to a Mining Tunnel Inspection Robot. RVSP 2011: 32-35
2000 – 2009
- 2008
- [c2]Junbin Gao, Michael Antolovich, Paul Wing Hing Kwan:
L1 LASSO Modeling and Its Bayesian Inference. Australasian Conference on Artificial Intelligence 2008: 318-324 - [c1]Richard Yi Da Xu, Junbin Gao, Michael Antolovich:
Novel methods for high-resolution facial image capture using calibrated PTZ and static cameras. ICME 2008: 45-48
Coauthor Index
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