ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies th ....ARC Centre of Excellence for Robotic Vision. Robots are vital to Australia's future prosperity in the face of high relative wages, low or decreasing productivity, and impending labour shortages. However the work and workplaces of our most important industries are unstructured and changeable and current robots are challenged by their inability to quickly, safely and reliably "see" and "understand" what is around them. The Centre's research will create the fundamental science and technologies that will allow robots to see as we do, and overcome the last barrier to the ubiquitous deployment of robots into society for the benefit of all.Read moreRead less
Accurate Performance Modelling and Prediction of Cluster Computers. The tools, methodologies and data produced by this project will assist
Australian academic and industrial organisations in choosing the most
cost-effective cluster configurations for their specific high
performance computing requirements. It will also help an Australian
company to compete with increasing strength against the major
multinationals. The project will also draw together and promote future
research links between ....Accurate Performance Modelling and Prediction of Cluster Computers. The tools, methodologies and data produced by this project will assist
Australian academic and industrial organisations in choosing the most
cost-effective cluster configurations for their specific high
performance computing requirements. It will also help an Australian
company to compete with increasing strength against the major
multinationals. The project will also draw together and promote future
research links between two major academic institutions in this field.
Finally, the project will provide high-level training in research,
with industrial grounding, in the high performance computing industry.
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Autocalibration without decimation. The insertion of computer generated characters into real footage, the removal of objects from video, and the recovery of 3-dimensional architectural or topographic models from photographs are amongst a growing number of processes used in industry which require highly accurate camera calibration. Autocalibration is thus a prerequisite for these and many other emerging image-based technologies. By developing expertise in this area, and particularly by enabling ....Autocalibration without decimation. The insertion of computer generated characters into real footage, the removal of objects from video, and the recovery of 3-dimensional architectural or topographic models from photographs are amongst a growing number of processes used in industry which require highly accurate camera calibration. Autocalibration is thus a prerequisite for these and many other emerging image-based technologies. By developing expertise in this area, and particularly by enabling more flexible and efficient means of autocalibration, we expect to provide Australian industry with a valuable improvement in the state of the art and a competitive edge in a number of important application areas.Read moreRead less
Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently ....Learning Robotic Navigation and Interaction from Object-based Semantic Maps. Our project aims to develop new learning algorithms that enable robots to perform high-complexity tasks that are currently impossible. Compared to existing methods that rely on low-level sensor data, we aim to achieve this by learning from a high-level graph representation of the environment that captures semantics, affordances, and geometry. The outcome would be robots capable of using human instructions to efficiently learn complex interaction and navigation behaviours that transfer to unseen environments. Our research should benefit new applications in domains of economic and societal importance that are currently too complex, unsafe, and uncertain for robot assistants, such as aged care, advanced manufacturing and domestic robotics.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE160100090
Funder
Australian Research Council
Funding Amount
$250,000.00
Summary
Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object ....Computational infrastructure for developing deep machine learning models. Computational infrastructure for developing deep machine learning models:
The computational infrastructure for developing deep machine learning models aims to enable new developments in machine learning of deep neural network models by providing the specialised computing necessary to train and evaluate the networks. In the last three years, deep networks have smashed previous performance ceilings for tasks such as object recognition in images, speech recognition and automatic translation, bringing the prospect of machine intelligence closer than ever. Modern machine learning techniques have had huge impact in the last decade in fields such as robotics, computer vision and data analytics. The facility would enable Australian researchers to develop, learn and apply deep networks to problems of national importance in robotic vision and big data analytics. Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE100100235
Funder
Australian Research Council
Funding Amount
$280,000.00
Summary
Accelerating Australia's large scale video surveillance research programmes. The research to be conducted using this infrastructure will bring immense benefits to Australia in terms of increased levels of public safety and in the protection of critical facilities from terrorism and other crimes, by developing better surveillance systems. This will provide both increases in measurable research outputs and opportunities for Australian business to commercialise these systems. The infrastructure wil ....Accelerating Australia's large scale video surveillance research programmes. The research to be conducted using this infrastructure will bring immense benefits to Australia in terms of increased levels of public safety and in the protection of critical facilities from terrorism and other crimes, by developing better surveillance systems. This will provide both increases in measurable research outputs and opportunities for Australian business to commercialise these systems. The infrastructure will accelerate the pace of surveillance research and development in Australia, enhancing the competitiveness of both Australia's researchers and the businesses that will commercialise these researchers' discoveries.Read moreRead less
Learning to see in 3D. The project aims to endow machine vision with an ability we, as humans, use almost constantly: to judge 3D properties from a 2D image. This extremely useful ability will be applied to digital images to obtain 3D measurements and aid in automating tasks such as mining, surveying, medical diagnosis, and visual effects in movies.
Intelligent and objective measurement of wool fibre diameter. More than a half million tones of wool produced in Australia per year are visually evaluated by human woolclassers. This fibre-classing process is subjective and heavily dependent on the experience of the classers. In this project, we will objectively measure wool fibre diameter by extracting features used by human woolclassers and by combining image processing and artificial intelligence. The fractal dimension calculated by fracta ....Intelligent and objective measurement of wool fibre diameter. More than a half million tones of wool produced in Australia per year are visually evaluated by human woolclassers. This fibre-classing process is subjective and heavily dependent on the experience of the classers. In this project, we will objectively measure wool fibre diameter by extracting features used by human woolclassers and by combining image processing and artificial intelligence. The fractal dimension calculated by fractal based texture analysis will be correlated to fibre diameter. This approach will provide an insight into an on farm and/or in shed objective measurement of wool fibre diameter.Read moreRead less
Parameterized Analysis of Bio-inspired Computing - From Theory to High Performing Algorithms. This project will establish the field of parameterised analysis of bio-inspired computing which includes prominent approaches such as evolutionary algorithms and ant colony optimisation. It will rigorously analyse features of instances of combinatorial optimisation problems and their impact on the runtime behaviour of bio-inspired computing methods. Furthermore, the project will design new bio-inspired ....Parameterized Analysis of Bio-inspired Computing - From Theory to High Performing Algorithms. This project will establish the field of parameterised analysis of bio-inspired computing which includes prominent approaches such as evolutionary algorithms and ant colony optimisation. It will rigorously analyse features of instances of combinatorial optimisation problems and their impact on the runtime behaviour of bio-inspired computing methods. Furthermore, the project will design new bio-inspired computing algorithms that make use of instance features and hardness characteristics. The results will advance the theoretical knowledge of bio-inspired computing, bridge the gap between theory and practice, and provide more powerful algorithms for complex optimisation problems occurring for example in the field of supply chain management for the mining industry.Read moreRead less
New Techniques for Artificial Neural Network Modelling in Hydrology. In recent years, artificial neural networks (ANNs) have demonstrated the potential to provide improved predictions when compared with the more traditional hydrological modelling techniques in a number of areas. These include the prediction of rainfall, streamflow and water quality parameters. However, one of the major difficulties associated with the application of ANNs is the lack of an established methodology for their design ....New Techniques for Artificial Neural Network Modelling in Hydrology. In recent years, artificial neural networks (ANNs) have demonstrated the potential to provide improved predictions when compared with the more traditional hydrological modelling techniques in a number of areas. These include the prediction of rainfall, streamflow and water quality parameters. However, one of the major difficulties associated with the application of ANNs is the lack of an established methodology for their design and implementation. This research will develop new methods for constructing ANN models and test them on a number of case studies so that the full potential and genuine utility of ANNs for solving hydrological problems can be assessed.Read moreRead less