ORCID Profile
0000-0002-2465-5971
Current Organisation
Deakin University
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Image Processing | Computer Vision | Artificial Intelligence and Image Processing | Pattern Recognition
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2019
Publisher: IEEE
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2010
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 12-2013
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: IEEE
Date: 30-11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer Science and Business Media LLC
Date: 03-04-2010
Publisher: Elsevier BV
Date: 02-2010
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2004
Abstract: In this paper, we explore how graph-spectral methods can be used to develop a new shape-from-shading algorithm. We characterize the field of surface normals using a weight matrix whose elements are computed from the sectional curvature between different image locations and penalize large changes in surface normal direction. Modeling the blocks of the weight matrix as distinct surface patches, we use a graph seriation method to find a surface integration path that maximizes the sum of curvature-dependent weights and that can be used for the purposes of height reconstruction. To smooth the reconstructed surface, we fit quadrics to the height data for each patch. The smoothed surface normal directions are updated ensuring compliance with Lambert's law. The processes of height recovery and surface normal adjustment are interleaved and iterated until a stable surface is obtained. We provide results on synthetic and real-world imagery.
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 09-2023
Publisher: IEEE
Date: 07-2010
Publisher: Elsevier BV
Date: 07-2014
Publisher: Association for Computing Machinery (ACM)
Date: 13-07-2023
DOI: 10.1145/3578516
Abstract: Underwater computer vision has attracted increasing attention in the research community due to the recent advances in underwater platforms such as of rovers, gliders, autonomous underwater vehicles (AUVs) , and the like, that now make possible the acquisition of vast amounts of imagery and video for applications such as bio ersity assessment, environmental monitoring, and search and rescue. Despite growing interest, underwater computer vision is still a relatively under-researched area, where the attention in the literature has been paid to the use of computer vision techniques for image restoration and reconstruction, where image formation models and image processing methods are used to recover colour corrected or enhanced images. This is due to the notion that these methods can be used to achieve photometric invariants to perform higher-level vision tasks such as shape recovery and recognition under the challenging and widely varying imaging conditions that apply to underwater scenes. In this paper, we review underwater computer vision techniques for image reconstruction, restoration, recognition, depth, and shape recovery. Further, we review current applications such as bio ersity assessment, management and protection, infrastructure inspection and AUVs navigation, amongst others. We also delve upon the current trends in the field and examine the challenges and opportunities in the area.
Publisher: Springer Science and Business Media LLC
Date: 29-06-2012
Publisher: Institution of Engineering and Technology (IET)
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2016
Publisher: Wiley
Date: 29-06-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2011
Publisher: Elsevier BV
Date: 11-2005
Publisher: Elsevier BV
Date: 07-2011
Publisher: IEEE
Date: 18-07-2021
Publisher: Elsevier BV
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: IEEE
Date: 08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2007
Abstract: This paper offers two new directions to shape-from-shading, namely the use of the heat equation to smooth the field of surface normals and the recovery of surface height using a low-dimensional embedding. Turning our attention to the first of these contributions, we pose the problem of surface normal recovery as that of solving the steady state heat equation subject to the hard constraint that Lambert's law is satisfied. We perform our analysis on a plane perpendicular to the light source direction, where the z component of the surface normal is equal to the normalized image brightness. The x - y or azimuthal component of the surface normal is found by computing the gradient of a scalar field that evolves with time subject to the heat equation. We solve the heat equation for the scalar potential and, hence, recover the azimuthal component of the surface normal from the average image brightness, making use of a simple finite difference method. The second contribution is to pose the problem of recovering the surface height function as that of embedding the field of surface normals on a manifold so as to preserve the pattern of surface height differences and the lattice footprint of the surface normals. We experiment with the resulting method on a variety of real-world image data, where it produces qualitatively good reconstructed surfaces.
Publisher: IEEE
Date: 2004
Publisher: Elsevier BV
Date: 08-2005
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 2004
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2011
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 09-2010
Publisher: IEEE
Date: 2004
Publisher: Elsevier BV
Date: 03-2007
Publisher: IEEE
Date: 2004
Publisher: Elsevier BV
Date: 09-2023
Publisher: Springer Nature Switzerland
Date: 2023
Publisher: Institution of Engineering and Technology (IET)
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17-05-2023
DOI: 10.36227/TECHRXIV.21500235
Abstract: Recurrent Neural Networks (RNN) are widespread for Video Super-Resolution (VSR) because of their proven ability to learn spatiotemporal inter-dependencies across the temporal dimension. Despite RNN’s ability to propagate memory across longer sequences of frames, vanishing gradient and error accumulation remain major obstacles to unidirectional RNNs in VSR. Several bi-directional recurrent models are suggested in the literature to alleviate this issue however, these models are only applicable to offline use cases due to heavy demands for computational resources and the number of frames required per input. This paper proposes a novel unidirectional recurrent model for VSR, namely “Replenished Recurrency with Dual-Duct” (R2D2), that can be used in an online application setting. R2D2 incorporates a recurrent architecture with a sliding-window-based local alignment resulting in a recurrent hybrid architecture. It also uses a dual-duct residual network for concurrent and mutual refinement of local features along with global memory for full utilisation of the information available at each timest . With novel modelling and sophisticated optimisation, R2D2 demonstrates competitive performance and efficiency despite the lack of information available at each time-st compared to its offline (bi-directional) counterparts. Ablation analysis confirms the additive benefits of the proposed sub-components of R2D2 over baseline RNN models.The PyTorch-based code for the R2D2 model will be released at R2D2 GitRepo.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2011
Publisher: IEEE
Date: 08-2014
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 08-2002
Publisher: ACM
Date: 13-10-2015
Publisher: Elsevier BV
Date: 08-2002
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 2004
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: IEEE
Date: 2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 06-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-11-2022
DOI: 10.36227/TECHRXIV.21500235.V1
Abstract: This is an original research article entitled “Online Video Super-Resolution using Unidirectional Recurrent Model”. Considering the critical constraints around video frames and resource availability in an online setting, this paper presents a new unidirectional video super-resolution (VSR) model with a recurrent architecture specifically designed for online applications. Many recent works in the video super-resolution domain focus on improving the super-resolution quality at the cost of computationally intense and input-heavy bidirectional modelling. To alleviate these drawbacks, we propose the Replenished Recurrency with Dual-Duct (R2D2) model which adopts unidirectional architecture to fully utilise local features and global memory available at each timest . The two variants – R2D2 and R2D2-lite presented in the paper generate state-of-the-art super-resolution quality at significantly optimised efficiency. This is believed an important step forward in real-world applications-inspired research in the video super-resolution domain.
Publisher: Springer Science and Business Media LLC
Date: 15-06-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 17-05-2023
DOI: 10.36227/TECHRXIV.21500235.V2
Abstract: Recurrent Neural Networks (RNN) are widespread for Video Super-Resolution (VSR) because of their proven ability to learn spatiotemporal inter-dependencies across the temporal dimension. Despite RNN’s ability to propagate memory across longer sequences of frames, vanishing gradient and error accumulation remain major obstacles to unidirectional RNNs in VSR. Several bi-directional recurrent models are suggested in the literature to alleviate this issue however, these models are only applicable to offline use cases due to heavy demands for computational resources and the number of frames required per input. This paper proposes a novel unidirectional recurrent model for VSR, namely “Replenished Recurrency with Dual-Duct” (R2D2), that can be used in an online application setting. R2D2 incorporates a recurrent architecture with a sliding-window-based local alignment resulting in a recurrent hybrid architecture. It also uses a dual-duct residual network for concurrent and mutual refinement of local features along with global memory for full utilisation of the information available at each timest . With novel modelling and sophisticated optimisation, R2D2 demonstrates competitive performance and efficiency despite the lack of information available at each time-st compared to its offline (bi-directional) counterparts. Ablation analysis confirms the additive benefits of the proposed sub-components of R2D2 over baseline RNN models.The PyTorch-based code for the R2D2 model will be released at R2D2 GitRepo.
Publisher: Elsevier BV
Date: 10-2012
Publisher: IEEE
Date: 06-2013
Location: United Kingdom of Great Britain and Northern Ireland
Location: Australia
Start Date: 2007
End Date: 12-2009
Amount: $231,090.00
Funder: Australian Research Council
View Funded Activity