Discovery Early Career Researcher Award - Grant ID: DE210101297
Funder
Australian Research Council
Funding Amount
$429,000.00
Summary
A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the e ....A novel, dictionary-free, multi-contrast MRI method for microscopic imaging. This project aims to develop a novel quantitative imaging technique for comprehensive in vitro and in vivo tissue characterisation on the microscopic scale. The technology innovated in the project could revolutionise microscopic imaging techniques by breaking through the sub-millimetre image resolution bottleneck of current magnetic resonance imaging (MRI) methods. This project expects to generate new knowledge in the emerging field of biological imaging and to deliver an integrated imaging platform for mapping various tissue microscopic components at the cellular level. Successful outcomes have the potential for commercialisation and will accelerate a range of fundamental science and engineering studies requiring imaging techniques.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220100188
Funder
Australian Research Council
Funding Amount
$438,582.00
Summary
Generating Plots with Dialogue Based Executable Semantic Parsing. This project aims to address the limited abilities of dialogue systems by developing new models and data collection techniques. The project expects to address a major gap in Natural Language Processing using a model that generates computer code and updates it in response to user requests. Expected outcomes of this project include a system that interacts with a user in plain English to analyse data, and efficient methods of trainin ....Generating Plots with Dialogue Based Executable Semantic Parsing. This project aims to address the limited abilities of dialogue systems by developing new models and data collection techniques. The project expects to address a major gap in Natural Language Processing using a model that generates computer code and updates it in response to user requests. Expected outcomes of this project include a system that interacts with a user in plain English to analyse data, and efficient methods of training the system with minimal expert input. This should provide significant benefits to research and business by broadening the accessibility and efficiency of data analysis, enabling faster and wiser decisions.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE190100539
Funder
Australian Research Council
Funding Amount
$408,000.00
Summary
Towards conversational vision-based Artificial Intelligence. This project aims to develop a novel learning framework, Vision-Ask-Answer-Act (V3A). This framework will allow a machine to perform a sequence of actions via a conversation with human users, based on intricate processing of not just visual input, but human-computer verbal exchanges. Artificial intelligence has great potential as a tool for economic productivity and daily tasks. Applications in cars and assistant robots, still in their ....Towards conversational vision-based Artificial Intelligence. This project aims to develop a novel learning framework, Vision-Ask-Answer-Act (V3A). This framework will allow a machine to perform a sequence of actions via a conversation with human users, based on intricate processing of not just visual input, but human-computer verbal exchanges. Artificial intelligence has great potential as a tool for economic productivity and daily tasks. Applications in cars and assistant robots, still in their early days, typically require significant expertise to use effectively. The outcomes of this project will push the boundary of vision-language research to produce a conversational intelligent agent that can be easily used in common situations across industry, transport, the medical sector, and at home.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120102900
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
WikiLinks: web-scale linking and fact extraction with Wikipedia. Wikipedia is the most popular web site for finding facts, but articles about local or specialist topics are often missing or unreliable. WikiLinks will use artificial intelligence to link names in text to corresponding Wikipedia articles, allowing us to automatically create and augment Wikipedia content by summarising existing material on the web.
Discovery Early Career Researcher Award - Grant ID: DE130100771
Funder
Australian Research Council
Funding Amount
$278,780.00
Summary
Automated system for bone texture analysis of osteoarthritis in hand radiographs. Early detection and prediction of hand osteoarthritis are not feasible by current methods of visual grading of hand radiographs. This project will develop a novel, fully automated system for selection of bone texture regions on hand radiographs and their analysis to address this problem.
Discovery Early Career Researcher Award - Grant ID: DE140100387
Funder
Australian Research Council
Funding Amount
$349,179.00
Summary
Mining Patterns and Changes of Wave Shapes for Efficiently Querying Periodic Data Streams. Many data streams change periodically, such as vital physiological parameters (for example, heart rate, arterial pressure and respiratory impedance) and seasonal environmental data streams (for example, temperature and turbidity of river water). However, the querying of periodic data streams faces great challenges, including the issue of critical signals being generally buried within massive data while cri ....Mining Patterns and Changes of Wave Shapes for Efficiently Querying Periodic Data Streams. Many data streams change periodically, such as vital physiological parameters (for example, heart rate, arterial pressure and respiratory impedance) and seasonal environmental data streams (for example, temperature and turbidity of river water). However, the querying of periodic data streams faces great challenges, including the issue of critical signals being generally buried within massive data while critical changes between similar wave shapes are difficult to recognise due to shifting, scaling and noise. This project will develop new mining algorithms to resolve these challenges by segmenting periodic wave shapes, discovering shape patterns and shape changes, and summarising raw data streams so that the summarised data can directly answer various user queries for efficiency.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100911
Funder
Australian Research Council
Funding Amount
$339,434.00
Summary
Accurate and online abnormality detection in multiple correlated time series. This study will develop a new kernel-based and online support vector regression method for real-time and correlated multiple time series and promote their use in critical applications, which will save money and lives. Examples include the detection of stock market crisis events and detection of patients' condition deterioration in the operating theatre.
Discovery Early Career Researcher Award - Grant ID: DE150101655
Funder
Australian Research Council
Funding Amount
$297,036.00
Summary
Discriminative detection and quantification of cancer imaging biomarkers. This project aims to develop a new framework for the detection and quantification of cancer biomarkers in diagnostic and histopathology images with discriminative modelling of intrinsic structures. The framework will be the first computerised solution to provide automated, quantitative annotations of cancer imaging biomarkers at the macroscopic and microscopic levels to support standardised reporting of image interpretatio ....Discriminative detection and quantification of cancer imaging biomarkers. This project aims to develop a new framework for the detection and quantification of cancer biomarkers in diagnostic and histopathology images with discriminative modelling of intrinsic structures. The framework will be the first computerised solution to provide automated, quantitative annotations of cancer imaging biomarkers at the macroscopic and microscopic levels to support standardised reporting of image interpretation. It will help to alleviate the inter-observer variability and time-consuming process of manual analysis. The project aims to advance fundamental biomedical imaging research in generalised visual structure extraction and classification, and enable large-scale translational research in systems pathology for personalised cancer care.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120101778
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Building change detection and map update using multispectral imagery and height data. This project will produce an effective building change detection procedure and a digital building map. Automatic building detection assists in taking possible precautions during natural disasters, whilst automatic building change detection facilitates an effective and efficient management of affected areas during and after the calamity.
Discovery Early Career Researcher Award - Grant ID: DE220101379
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
Towards Transferable Visual Understanding in the Real World. This project aims to investigate how to improve the transferability of visual understanding algorithm and system in the real-world applications. This project expects to innovate and advance knowledge in the fields of visual transfer learning and generalizable visual representation learning. Expected outcomes of this project include techniques and algorithms to make the visual understanding system robust to diverse real-world scenarios. ....Towards Transferable Visual Understanding in the Real World. This project aims to investigate how to improve the transferability of visual understanding algorithm and system in the real-world applications. This project expects to innovate and advance knowledge in the fields of visual transfer learning and generalizable visual representation learning. Expected outcomes of this project include techniques and algorithms to make the visual understanding system robust to diverse real-world scenarios. This project should provide significant benefits, such as improving the robustness and safety of autonomous vehicles in transportation area, and reducing the cost of destructive data collection for intelligent fault detection in advanced manufacturing area.Read moreRead less