Discovery Early Career Researcher Award - Grant ID: DE140100180
Funder
Australian Research Council
Funding Amount
$394,305.00
Summary
Advancing Dense 3D Reconstruction of Non-rigid Scenes by Using a Moving Camera. This project will advance the fundamental research in geometric computer vision and develop a new framework for efficient dense three-dimensional reconstruction of non-rigid scenes by using a moving camera. It is expected that this project will bring about breakthroughs in geometric computer vision with many daily applications, including three-dimensional natural human-computer interaction, three-dimensional reconstr ....Advancing Dense 3D Reconstruction of Non-rigid Scenes by Using a Moving Camera. This project will advance the fundamental research in geometric computer vision and develop a new framework for efficient dense three-dimensional reconstruction of non-rigid scenes by using a moving camera. It is expected that this project will bring about breakthroughs in geometric computer vision with many daily applications, including three-dimensional natural human-computer interaction, three-dimensional reconstruction from historical movies and three-dimensional realistic animations. Its outcomes will enable users to capture and manipulate their surrounding dynamic world in three-dimensions easily and conveniently. This project will alleviate many of the major difficulties (dense correspondences, long sequences, complex deformations) with conventional non-rigid reconstruction methods.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150100363
Funder
Australian Research Council
Funding Amount
$330,000.00
Summary
The cocktail party problem: Advancing binaural localisation techniques. This project aims to advance fundamental research in source localisation by using a binaural system with two sensors to mimic human listening capabilities. It will provide new theory of source localisation features, novel signal processing techniques and design of binaural devices for localising sound sources in a cluttered acoustic environment. New technologies developed from this project will endeavour to lead to further d ....The cocktail party problem: Advancing binaural localisation techniques. This project aims to advance fundamental research in source localisation by using a binaural system with two sensors to mimic human listening capabilities. It will provide new theory of source localisation features, novel signal processing techniques and design of binaural devices for localising sound sources in a cluttered acoustic environment. New technologies developed from this project will endeavour to lead to further development of binaural audio research and will have a broad range of applications, such as hearing aids, personal sound amplification products and humanoid robots. The project aims to enable people wearing binaural devices or robots having two artificial ears to localise sounds and to follow a conversation in realistic situations.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100317
Funder
Australian Research Council
Funding Amount
$376,000.00
Summary
Advancing uncertainty prioritisation in water resource management. This project aims to develop a holistic framework for prioritisation of uncertainties in Integrated Water Resource Assessment and Management to assist water resource analysts and planners to improve sustainability of water resource management outcomes. The framework will help scientists, consultants, policy makers and water users better select which sources of uncertainty to address in their work, with what resources, and what me ....Advancing uncertainty prioritisation in water resource management. This project aims to develop a holistic framework for prioritisation of uncertainties in Integrated Water Resource Assessment and Management to assist water resource analysts and planners to improve sustainability of water resource management outcomes. The framework will help scientists, consultants, policy makers and water users better select which sources of uncertainty to address in their work, with what resources, and what methods. Expected outcomes are novel analytical methods to evaluate current practice in uncertainty prioritisation, communicate when and how to use established and novel uncertainty management techniques, and improve prioritisation of uncertainty using proof-of-concept model-based analyses. The project should improve decision making in policy and industry, and societal and environmental outcomes of water management.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200101283
Funder
Australian Research Council
Funding Amount
$400,998.00
Summary
Data synthesis to quantitatively understand and improve vision systems. This project aims to build high-fidelity synthetic data, to understand how a machine vision system reacts to environmental factors and consequently improve the ability of the system to generalise in the real world. This project expects to generate new knowledge in the area of computer vision using innovative techniques of data synthesis, analysis, and domain adaptation. The expected outcomes include new scientific discoverie ....Data synthesis to quantitatively understand and improve vision systems. This project aims to build high-fidelity synthetic data, to understand how a machine vision system reacts to environmental factors and consequently improve the ability of the system to generalise in the real world. This project expects to generate new knowledge in the area of computer vision using innovative techniques of data synthesis, analysis, and domain adaptation. The expected outcomes include new scientific discoveries and domain adaptation algorithms derived from synthetic data for real-world applications. The benefits are expected to be widespread across sectors such as transportation, security, and manufacturing, including safer robotic navigation, defect detection, and smart video surveillance to improve community safety.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102948
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Interactive computer vision for image interpretation. This project aims at pushing forward the fundamental research in interactive computer vision. The outcome of this project will enable reliable and efficient solutions to real world image interpretation tasks, such as medical image analysis, financial document processing, and impact evaluation from natural disasters.
Discovery Early Career Researcher Award - Grant ID: DE240100206
Funder
Australian Research Council
Funding Amount
$403,000.00
Summary
Probing ultralight bosons with black holes and gravitational waves. This project aims to search for gravitational waves from ultralight boson clouds around black holes and to investigate the boson properties. It expects to generate new knowledge on currently undiscovered particles by combining cutting-edge theories and innovative signal-processing techniques. These particles are predicted to solve problems in particle and high-energy physics and are compelling dark matter candidates. Expected ou ....Probing ultralight bosons with black holes and gravitational waves. This project aims to search for gravitational waves from ultralight boson clouds around black holes and to investigate the boson properties. It expects to generate new knowledge on currently undiscovered particles by combining cutting-edge theories and innovative signal-processing techniques. These particles are predicted to solve problems in particle and high-energy physics and are compelling dark matter candidates. Expected outcomes include high-profile constraints on the particle properties and potential detection of new particles, new data-analysis techniques, and significantly enhanced capacity to build international and interdisciplinary collaborations. These should bring significant benefits to fundamental physics and cosmology.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE230101567
Funder
Australian Research Council
Funding Amount
$453,054.00
Summary
Listening to Nature: Transforming Bioacoustics through Spatial Audio. This project aims to research new 3D spatial audio processing techniques to analyse natural sounds for environmental conservation, while meeting the tasks, demands and data characteristics inherent to bioacoustics. Expected outcomes include new, accurate and efficient bioacoustics computation technologies, generalisable across different terrestrial regions, species types and environment changes. These could dramatically enhanc ....Listening to Nature: Transforming Bioacoustics through Spatial Audio. This project aims to research new 3D spatial audio processing techniques to analyse natural sounds for environmental conservation, while meeting the tasks, demands and data characteristics inherent to bioacoustics. Expected outcomes include new, accurate and efficient bioacoustics computation technologies, generalisable across different terrestrial regions, species types and environment changes. These could dramatically enhance the efficacy of current bioacoustic monitoring systems while opening up new research directions. Resulting technology could be adopted for immediate tasks like the monitoring of bushfire recovery efforts, and more generally, for the management and conservation of Australian natural resources.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100628
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Machine vision techniques for solar power forecasting and generation. This project aims to advance the research in short-term solar power forecasting and optimise the generation process using machine vision techniques. This project will use cameras to capture images of sky and mirror surfaces of heliostats. The scientific novelties are the exploration of geometry-aware feature representations for solar power prediction and building three-dimensional models of mirror surfaces of heliostats to opt ....Machine vision techniques for solar power forecasting and generation. This project aims to advance the research in short-term solar power forecasting and optimise the generation process using machine vision techniques. This project will use cameras to capture images of sky and mirror surfaces of heliostats. The scientific novelties are the exploration of geometry-aware feature representations for solar power prediction and building three-dimensional models of mirror surfaces of heliostats to optimise the solar power generation process. The outcome is a working prototype to boost the solar power forecasting accuracy and a three-dimensional reconstruction system to be helpful for the solar power generation. These outcomes will highly benefit the short-term solar power forecasting, generation and electricity grid management systems.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101605
Funder
Australian Research Council
Funding Amount
$289,000.00
Summary
Composing machine learning via market mechanisms. This project aims to better understand connections between learning algorithms and markets as aggregators of information and develop new, principled techniques for combining predictions. This will improve our ability to construct systems that make predictions based on multiple, complex and structured sources of data.
Discovery Early Career Researcher Award - Grant ID: DE170100128
Funder
Australian Research Council
Funding Amount
$395,000.00
Summary
Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts ....Information processing in the brain. This project aims to understand the brain's functional organisation by developing non-invasive methods to characterise connectivity between interacting brain regions. No model-based methods to compute directional coupling between brain regions can be applied to large scale networks for resting state functional MRI data. This capability would be a major breakthrough in neuroimaging, given uninformative (non-directional) network connectivity analysis restricts research. This project is expected to advance our understanding of information processing in the brain by providing a mechanistic approach to functional integration.Read moreRead less