Active Segmentation for Cooperative Mobile Robots in Outdoor Environments. The objective of this project is to develop a principled understanding of how to improve the segmentation of three-dimensional range data in a cooperative manner by judiciously choosing future sensor viewpoints of a team of mobile robots. The viewpoint of the robot affects both the density of three-dimensional data points and the areas that are unobservable due to occlusions. Project outcomes will open up a whole new appr ....Active Segmentation for Cooperative Mobile Robots in Outdoor Environments. The objective of this project is to develop a principled understanding of how to improve the segmentation of three-dimensional range data in a cooperative manner by judiciously choosing future sensor viewpoints of a team of mobile robots. The viewpoint of the robot affects both the density of three-dimensional data points and the areas that are unobservable due to occlusions. Project outcomes will open up a whole new approach to the process of autonomously gathering information about objects in outdoor environments, will synergise with existing classification and motion planning methods, and will support Australia's continued role as a leader in field robotics research.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE160100630
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Relating function of complex networks to structure using information theory. This project aims to investigate networks in order to translate network function to a universal language of information flows. Network science has used common tools to reveal universal connection structures within various biological and man-made networks – our brains, social networks and power grids are all networks of interacting components. Yet there is no common method to study the function of these networks and how ....Relating function of complex networks to structure using information theory. This project aims to investigate networks in order to translate network function to a universal language of information flows. Network science has used common tools to reveal universal connection structures within various biological and man-made networks – our brains, social networks and power grids are all networks of interacting components. Yet there is no common method to study the function of these networks and how such function is coupled with structure. This project aims to relate network structure to function by using measures of information processing as a generally-applicable framework. This will deliver a theory of how structure gives rise to dynamics and how structure can be optimised for desired dynamics.Read moreRead less
An automatic markerless three-dimensional (3D) motion analysis system for aquatic environments. Australia's sporting performance on the international stage forms an integral part of the psyche of Australians. This project applies latest 3D imaging and biomechanical techniques to quantify swimmers' movement patterns, thereby ensuring Australia's continued elite sporting success and consolidating its current lead in world class technologies.
Advanced Monte Carlo Methods for Spatial Processes. The modeling and analysis of spatial data relies more and more on sophisticated Monte Carlo simulation methods. However, with the growing complexity of today's spatial data, traditional Monte Carlo methods increasingly face difficulties in terms of speed and accuracy. The aim of this project is to develop new theory and applications at the interface of Monte Carlo methods and spatial statistics, building upon exciting theoretical and computatio ....Advanced Monte Carlo Methods for Spatial Processes. The modeling and analysis of spatial data relies more and more on sophisticated Monte Carlo simulation methods. However, with the growing complexity of today's spatial data, traditional Monte Carlo methods increasingly face difficulties in terms of speed and accuracy. The aim of this project is to develop new theory and applications at the interface of Monte Carlo methods and spatial statistics, building upon exciting theoretical and computational advances in both areas in recent years. The research will stimulate the design of microscopic and macroscopic complex spatial structures with superior properties, such as composite materials, solar cells, telecommunication networks, mining operations, and road systems.Read moreRead less
Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will desig ....Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will design very efficient binary networks, enabling large-scale deployment of deep learning to mobile devices. Thus this project will overcome two primary limitations of deep learning generally, however, and will greatly increase its already impressive domain of practical application.Read moreRead less
Search strategy optimisation by theory, functional analysis and simulation. This project aims to develop a novel computational platform, based on mathematical, statistical and physical theory, as well as advanced simulations, enabling the quantitative prediction of the optimal search strategy to be adopted by populations of agents searching for scarce targets in any given environment. This could lead to significant impacts on breakthrough developments in cancer immunotherapy, search and rescue r ....Search strategy optimisation by theory, functional analysis and simulation. This project aims to develop a novel computational platform, based on mathematical, statistical and physical theory, as well as advanced simulations, enabling the quantitative prediction of the optimal search strategy to be adopted by populations of agents searching for scarce targets in any given environment. This could lead to significant impacts on breakthrough developments in cancer immunotherapy, search and rescue robotics, ecological and environmental management, and developmental biology.Read moreRead less
Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imagin ....Deep Weak Learning for Morphology Analysis of Micro and Nanoscale Images. This project will develop novel methods for automated discovery and quantification of image phenotypes from micro and nanoscale images. The outcome will be an advance of the state of the art in biomedical image analysis with a particular focus on generalized weakly-supervised deep learning models for morphological feature representation. The methodologies will transform the deep learning pipeline for real biomedical imaging scenarios with high heterogeneity and limited training data. The frameworks will facilitate high-throughput processing for a wide range of microscopy image modalities and biological applications, and potentially become the next generation computational platform to support fundamental research in human biology.Read moreRead less
Enhanced Through-Wall Imaging using Bayesian Compressive Sensing. The aim of this project is to develop radar imaging techniques which enable us to 'see' objects behind walls and opaque materials. The major intended breakthrough is the ability to image objects behind walls and inside buildings or enclosed structures without accessing the scene. Novel signal and image processing algorithms, based on Bayesian compressive sensing, will be developed to enhance image quality and resolution, improve s ....Enhanced Through-Wall Imaging using Bayesian Compressive Sensing. The aim of this project is to develop radar imaging techniques which enable us to 'see' objects behind walls and opaque materials. The major intended breakthrough is the ability to image objects behind walls and inside buildings or enclosed structures without accessing the scene. Novel signal and image processing algorithms, based on Bayesian compressive sensing, will be developed to enhance image quality and resolution, improve speed of operation, and reduce the cost and time of data acquisition and processing. Many applications are expected to benefit from this research including search and rescue, surveillance, security, and defence. The research outcomes are expected to enhance the capabilities of the Australian armed forces, counter-terrorism, police and law-enforcement agencies.Read moreRead less
3D Vision Geometric Optimisation in Deep Learning. This project aims to develop a methodology for integrating the algorithms of 3D Vision Geometry and Optimization into the framework of Machine Learning and demonstrate the wide applicability of the new methods on a variety of challenging fundamental problems in Computer Vision. These include 3D geometric scene understanding, and estimation and prediction of human 2D/3D pose and activity. Applications of this technology are to be found in Intell ....3D Vision Geometric Optimisation in Deep Learning. This project aims to develop a methodology for integrating the algorithms of 3D Vision Geometry and Optimization into the framework of Machine Learning and demonstrate the wide applicability of the new methods on a variety of challenging fundamental problems in Computer Vision. These include 3D geometric scene understanding, and estimation and prediction of human 2D/3D pose and activity. Applications of this technology are to be found in Intelligent Transportation, Environment Monitoring, and Augmented Reality, applicable in smart-city planning and medical applications such as computer-enhanced surgery. The goal is to build Australia's competitive advantage in the forefront of ICT research and technology innovation.Read moreRead less
Foundations and advanced algorithms for topological image processing. Building on new links between the mathematical discipline of homology and digital images, this project develops a new class of topology-driven image analysis techniques that will improve the accuracy and reliability of predictions made from the powerful new generation of three dimensional microscopes.