Multiview video coding using cuboid data compression. This project investigates novel approaches to multiview video coding that use new data compression techniques and explicit occlusion handling. These new approaches complement the state-of-the-art, improving interactivity with instantaneous view change and VCR functionality, reducing encoding complexity, and increasing compression efficiency.
Structural-functional connectivity in the brain. This project aims to develop magnetic resonance imaging analysis methods to non-invasively study brain connectivity. Recent advances in imaging can comprehensively describe the brain’s complex network of functional and structural connections (the brain ‘connectome’). This project will simultaneously investigate structural and functional connectivity, and characterise the dynamic properties of the connectome using graph-theoretic approaches. This p ....Structural-functional connectivity in the brain. This project aims to develop magnetic resonance imaging analysis methods to non-invasively study brain connectivity. Recent advances in imaging can comprehensively describe the brain’s complex network of functional and structural connections (the brain ‘connectome’). This project will simultaneously investigate structural and functional connectivity, and characterise the dynamic properties of the connectome using graph-theoretic approaches. This project should give neuroscientists computational tools to comprehensively map the network architecture of the human brain.Read moreRead less
Methodologies for face recognition under varying imaging conditions. Face recognition systems are heavily dependent on the nature of the input to the system. Variability in appearance due to changes in illumination, expression, pose, etc. can reduce the recognition results of the existing systems. The aim of this project is to develop new techniques to improve the recognition accuracy in natural environment where unwanted image variations exist. The development of such techniques will be of grea ....Methodologies for face recognition under varying imaging conditions. Face recognition systems are heavily dependent on the nature of the input to the system. Variability in appearance due to changes in illumination, expression, pose, etc. can reduce the recognition results of the existing systems. The aim of this project is to develop new techniques to improve the recognition accuracy in natural environment where unwanted image variations exist. The development of such techniques will be of great importance to Australia's security and safety. The outcome of this research will provide the first steps towards formulating the next generation recognition systems that will improve the suitability of the face recognition for use in security, surveillance, intelligent robotics, banking, and smart environments.Read moreRead less
Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising ap ....Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising approach, but its accuracy is far from satisfactory. The software systems developed in this project will be used in structural identification of target proteins in drug design. This will make drug design process more efficient, saving time and cost, potentially saving lives.Read moreRead less
Efficient multi-view video coding with cuboids and base anchored models. This project aims to address current deficiencies in multi-view video coding technology to achieve the ultra-compression efficiency demanded by increasing display resolutions and synchronised viewpoints. The project expects to generate new knowledge, by moving from the current pixel-centric approach to methods that concentrate information common to many view-frames. The project is expected to improve compression of audio-vi ....Efficient multi-view video coding with cuboids and base anchored models. This project aims to address current deficiencies in multi-view video coding technology to achieve the ultra-compression efficiency demanded by increasing display resolutions and synchronised viewpoints. The project expects to generate new knowledge, by moving from the current pixel-centric approach to methods that concentrate information common to many view-frames. The project is expected to improve compression of audio-visual services that are of great interest to international standards bodies and industry, while facilitating free interaction and augmented reality. This project will provide significant benefits to broadcast, entertainment, surveillance and health industries and position Australia as a world leader in this field.Read moreRead less
Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored ....Personalised Learning for Per-pixel Prediction Tasks in Image Analysis. AI-assisted image segmentation & synthesis are very challenging and usually require pixel-level labelling (per-pixel prediction) that is costly to obtain. The small amount of labels makes it difficult to train an “optimal” unified model for varied data as conventional methods did. This project aims to develop a new paradigm “personalised learning” to tackle this problem, where each image could be dealt with a model tailored to individual characteristics. The success of this project could significantly advance the fundamental research in image analysis. Expected outcomes include new knowledge and algorithms for image analysis, which could benefit fields like biology and archaeology, where labeled images are hard to attain and scarce.Read moreRead less
Artificial intelligence meets wireless sensor networks: filling the gaps between sensors using spatial reasoning. Monitoring potential disaster regions and integrating available information with expert knowledge can prevent disasters and save many lives. The outcome of our project is one of the key components for intelligent systems that can autonomously monitor the environment, make the correct inferences and issue appropriate warnings and recommendations.
Robust AI Planning for Hybrid Systems. Automated planning, a core area of Artificial Intelligence, can effectively deal with the automatic synthesis of optimised action strategies for discrete system models. Extending the reach of planning to hybrid discrete/continuous systems, under exogenous uncertainty, is a widely open problem which this project will address. This will enable the proactive, and therefore more effective, management of microgrids and other cyber-physical systems, based on fore ....Robust AI Planning for Hybrid Systems. Automated planning, a core area of Artificial Intelligence, can effectively deal with the automatic synthesis of optimised action strategies for discrete system models. Extending the reach of planning to hybrid discrete/continuous systems, under exogenous uncertainty, is a widely open problem which this project will address. This will enable the proactive, and therefore more effective, management of microgrids and other cyber-physical systems, based on forecast information.Read moreRead less
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less
Data structures which change with time, a machine learning approach. Visibility of web pages, based on page importance, on the Internet controls their accessibility by users which is critical for e-Commerce applications. The page importance depends on its contents and its link structure to other web pages, both of which can be time varying. This project proposes a novel model in which time varying aspects of the changes to contents and their link structures are captured, thus allowing us a bette ....Data structures which change with time, a machine learning approach. Visibility of web pages, based on page importance, on the Internet controls their accessibility by users which is critical for e-Commerce applications. The page importance depends on its contents and its link structure to other web pages, both of which can be time varying. This project proposes a novel model in which time varying aspects of the changes to contents and their link structures are captured, thus allowing us a better understanding of how these influence the page importance over time. It will also allow us insight on how to improve the visibility of web pages.Read moreRead less