ORCID Profile
0000-0002-6937-6300
Current Organisation
Monash University
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Cybersecurity and privacy not elsewhere classified | Signal Processing | Deep learning | Computer Graphics | Computer vision | Machine learning | Computer Vision | Artificial Intelligence and Image Processing
Manufacturing not elsewhere classified | Electronic Information Storage and Retrieval Services | Animation and Computer Generated Imagery Services |
Publisher: IEEE
Date: 12-2013
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 02-2007
Publisher: IEEE
Date: 03-2020
Publisher: IEEE
Date: 09-2013
Publisher: IEEE
Date: 06-2014
Publisher: IEEE
Date: 2015
Publisher: Society for Neuroscience
Date: 11-03-2015
DOI: 10.1523/JNEUROSCI.3741-14.2015
Abstract: Cortical cells integrate synaptic input from multiple sources, but how these different inputs are distributed across in idual neurons is largely unknown. Differences in input might account for erse responses in neighboring neurons during behavior. We present a strategy for comparing the strengths of multiple types of input onto the same neuron. We developed methods for independent dual-channel photostimulation of synaptic inputs using ChR2 together with ReaChR, a red-shifted channelrhodopsin. We used dual-channel photostimulation to probe convergence of sensory information in the mouse primary motor cortex. Input from somatosensory cortex and thalamus converges in in idual neurons. Similarly, inputs from distinct somatotopic regions of the somatosensory cortex are integrated at the level of single motor cortex neurons. We next developed a ReaChR transgenic mouse under the control of both Flp- and Cre-recombinases that is an effective tool for circuit mapping. Our approach to dual-channel photostimulation enables quantitative comparison of the strengths of multiple pathways across all length scales of the brain.
Publisher: IEEE
Date: 09-2012
DOI: 10.1109/AVSS.2012.23
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/CVPR.2013.65
Publisher: Elsevier BV
Date: 03-2015
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Springer International Publishing
Date: 2014
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 06-2014
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Springer Science and Business Media LLC
Date: 19-08-2008
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer Science and Business Media LLC
Date: 2011
DOI: 10.1155/2011/790598
Publisher: IEEE
Date: 03-2020
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 09-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 06-2018
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 10-2019
Publisher: IEEE
Date: 06-2020
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Oxford University Press (OUP)
Date: 23-06-2018
Publisher: IEEE
Date: 03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 12-2019
Publisher: IEEE
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Elsevier BV
Date: 08-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: IGI Global
Date: 2012
DOI: 10.4018/978-1-4666-1833-6.CH007
Abstract: Recognizing objects based on their appearance (visual recognition) is one of the most significant abilities of many living creatures. In this study, recent advances in the area of automated object recognition are reviewed the authors specifically look into several learning frameworks to discuss how they can be utilized in solving object recognition paradigms. This includes reinforcement learning, a biologically-inspired machine learning technique to solve sequential decision problems and transductive learning, and a framework where the learner observes query data and potentially exploits its structure for classification. The authors also discuss local and global appearance models for object recognition, as well as how similarities between objects can be learnt and evaluated.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: IEEE
Date: 10-2017
Publisher: Springer International Publishing
Date: 2018
Publisher: Association for Computing Machinery (ACM)
Date: 30-11-2019
DOI: 10.1145/3342227
Abstract: Characterizing subtle facial movements from videos is one of the most intensive topics in computer vision research. It is, however, challenging, since (1) the intensity of subtle facial muscle movement is usually low, (2) the duration may be transient, and (3) datasets containing spontaneous subtle movements with reliable annotations are painful to obtain and often of small sizes. This article is targeted at addressing these problems for characterizing subtle facial movements from both the aspects of motion elucidation and description. First, we propose an efficient method for elucidating hidden and repressed movements to make them easier to get noticed. We explore the feasibility of linearizing motion magnification and temporal interpolation, which is obscured by the architecture of existing methods. On this basis, we propose a consolidated framework, termed MOTEL, to expand temporal duration and lify subtle facial movements simultaneously. Second, we make our contribution to dynamic description. One major challenge is to capture the intrinsic temporal variations caused by movements and omit extrinsic ones caused by different in iduals and various environments. To diminish the influences of such extrinsic ersity, we propose the tangent delta descriptor to characterize the dynamics of short-term movements using the differences between points on the tangent spaces to the manifolds, rather than the points themselves. We then relax the trajectory-smooth assumption of the conventional manifold-based trajectory modeling methods and incorporate the tangent delta descriptor with the sequential inference approaches to cover the period of facial movements. The proposed motion modeling approach is validated by a series of experiments on publicly available datasets in the tasks of micro-expression recognition and visual speech recognition .
Publisher: IEEE
Date: 2004
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: IEEE
Date: 06-2016
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 12-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: IEEE
Date: 10-2019
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Oxford University Press (OUP)
Date: 15-02-2018
DOI: 10.1093/BRAIN/AWY008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: IEEE
Date: 08-2010
Publisher: IEEE
Date: 2013
Publisher: IEEE
Date: 09-2012
Publisher: IEEE
Date: 2013
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/CVPR.2013.17
Publisher: Elsevier BV
Date: 10-2017
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 04-2015
Publisher: Springer Science and Business Media LLC
Date: 07-06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: IEEE
Date: 06-2020
Publisher: IEEE
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer International Publishing
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 04-2017
Publisher: Springer International Publishing
Date: 2015
Publisher: IEEE
Date: 06-2019
Publisher: Elsevier BV
Date: 08-2021
Publisher: IEEE
Date: 06-2018
Publisher: Springer Science and Business Media LLC
Date: 13-07-2023
Publisher: IGI Global
Date: 2013
DOI: 10.4018/978-1-4666-3994-2.CH045
Abstract: Recognizing objects based on their appearance (visual recognition) is one of the most significant abilities of many living creatures. In this study, recent advances in the area of automated object recognition are reviewed the authors specifically look into several learning frameworks to discuss how they can be utilized in solving object recognition paradigms. This includes reinforcement learning, a biologically-inspired machine learning technique to solve sequential decision problems and transductive learning, and a framework where the learner observes query data and potentially exploits its structure for classification. The authors also discuss local and global appearance models for object recognition, as well as how similarities between objects can be learnt and evaluated.
Publisher: IEEE
Date: 11-2013
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Springer International Publishing
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2015
Publisher: IEEE
Date: 05-2007
Publisher: IEEE
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Inderscience Publishers
Date: 2011
Publisher: IEEE
Date: 07-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 12-2013
Start Date: 06-2015
End Date: 03-2019
Amount: $370,100.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2023
End Date: 12-2025
Amount: $420,000.00
Funder: Australian Research Council
View Funded Activity