Semantic change detection through large-scale learning. This project aims to develop technologies which understand the content of images before higher-level analysis is performed. This approach is intended to allow more accurate and reliable decisions to be made using automated image analysis than has previously been possible. The project will particularly investigate the detection of change in the contents of an image.
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
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
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.Read moreRead less
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
Vision performance in relationship to spectacle lens design. Refractive errors such as short-sightedness, long-sightedness or presbyopia (age related decline in near vision) are the leading causes of visual impairment in the world. Of these, presbyopia affects almost 100% of the population above 45 years of age. This represents over 40% of all Australians. Although spectacles provide a safe and easy means of correcting refractive errors, they affect quality of life due to distorted vision, disco ....Vision performance in relationship to spectacle lens design. Refractive errors such as short-sightedness, long-sightedness or presbyopia (age related decline in near vision) are the leading causes of visual impairment in the world. Of these, presbyopia affects almost 100% of the population above 45 years of age. This represents over 40% of all Australians. Although spectacles provide a safe and easy means of correcting refractive errors, they affect quality of life due to distorted vision, discomfort such as head and neck ache and cosmetic effects. The goals of the project are to better understand the visual performance of young and old people who wear glasses and to develop improved spectacle lens designs to provide clear and comfortable vision over a range of distances.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130101775
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Distributed large-scale optimisation methods in computer vision. With the number of images and video available over the internet reaching billions and growing, the need for new tools for handling and interpreting such huge amounts of data is quickly becoming apparent. This project will focus on developing new optimisation methods for efficiently computing solutions for a broad class of large-scale problems.
Special Research Initiatives - Grant ID: SR0354735
Funder
Australian Research Council
Funding Amount
$10,000.00
Summary
Australian Network on Microelectronics, Optoelectronics and Microelectromechanical Systems. The Network will encompass semiconductor microelectronics, optoelectronics, sensors and microelectromechanical systems (MEMS). Fundamental research in these areas enables the technological advances that underpin rapidly developing industries such as information and telecommunications technologies, defence, aerospace, medicine, and remote sensing. Exciting challenges exist in designing new devices that exp ....Australian Network on Microelectronics, Optoelectronics and Microelectromechanical Systems. The Network will encompass semiconductor microelectronics, optoelectronics, sensors and microelectromechanical systems (MEMS). Fundamental research in these areas enables the technological advances that underpin rapidly developing industries such as information and telecommunications technologies, defence, aerospace, medicine, and remote sensing. Exciting challenges exist in designing new devices that exploit unique semiconductor systems and technologies. By sharing capabilities and resources (both capital and human), the network will enable the issues associated with such novel materials and devices to be addressed in a targeted manner. The network will also guarantee the ongoing future of research in the area by actively involving early career researchers and postgraduate students.Read moreRead less
ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this te ....ARC Research Network on Intelligent Sensors, Sensor Networks and Information Processing. Sensor networks, a collection of diverse sensors interconnected via an ad-hoc communication network, are identified as one of the key technologies that over the next two decades will change the way we live. This research network brings together an interdisciplinary team of outstanding Australian researchers representing all the key disciplines required to successfully deploy sensor networks and links this team with the foremost international authorities and leading industry players in the area of sensor networks. This research network will guide collaborative research that will ensure Australia to play a world leading role in sensor network development and implementation.
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A process model of visual working memory. This project aims to develop a process model of encoding of items into memory. Working memory is central to almost all cognitive functions, but little is known about short-term memory for visual information. Progress in this area is slow because of a focus on models that do not specify the processes underlying memory, and no model explains the processes that would limit the number of items the memory can hold to four. A process model is expected to addre ....A process model of visual working memory. This project aims to develop a process model of encoding of items into memory. Working memory is central to almost all cognitive functions, but little is known about short-term memory for visual information. Progress in this area is slow because of a focus on models that do not specify the processes underlying memory, and no model explains the processes that would limit the number of items the memory can hold to four. A process model is expected to address fundamental issues in visual working memory.Read moreRead less