ARDC Research Link Australia Research Link Australia   BETA Research
Link
Australia
  • ARDC Newsletter Subscribe
  • Contact Us
  • Home
  • About
  • Feedback
  • Explore Collaborations
  • Researcher
  • Funded Activity
  • Organisation
  • Researcher
  • Funded Activity
  • Organisation
  • Researcher
  • Funded Activity
  • Organisation

Need help searching? View our Search Guide.

Advanced Search

Current Selection
Australian State/Territory : QLD
Research Topic : missing data
Field of Research : Computer Vision
Australian State/Territory : SA
Clear All
Filter by Field of Research
Artificial Intelligence and Image Processing (5)
Computer Vision (5)
Pattern Recognition and Data Mining (5)
Image Processing (2)
Adaptive Agents and Intelligent Robotics (1)
Biomedical Engineering not elsewhere classified (1)
Microelectromechanical Systems (MEMS) (1)
Filter by Socio-Economic Objective
Expanding Knowledge in the Information and Computing Sciences (3)
Expanding Knowledge in Engineering (2)
Computer Software and Services not elsewhere classified (1)
Expanding Knowledge in Technology (1)
Fisheries - Aquaculture not elsewhere classified (1)
Information Processing Services (incl. Data Entry and Capture) (1)
Information Services not elsewhere classified (1)
Plant Production and Plant Primary Products not elsewhere classified (1)
Filter by Funding Provider
Australian Research Council (5)
Filter by Status
Closed (4)
Active (1)
Filter by Scheme
Discovery Projects (2)
Discovery Early Career Researcher Award (1)
Industrial Transformation Research Hubs (1)
Linkage Infrastructure, Equipment and Facilities (1)
Filter by Country
Australia (5)
Filter by Australian State/Territory
QLD (5)
SA (5)
NSW (3)
ACT (2)
VIC (2)
WA (2)
TAS (1)
  • Researchers (2)
  • Funded Activities (5)
  • Organisations (0)
  • Funded Activity

    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.
    More information
    Funded Activity

    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 more Read less
    More information
    Funded Activity

    Discovery Projects - Grant ID: DP140102794

    Funder
    Australian Research Council
    Funding Amount
    $295,000.00
    Summary
    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 more Read less
    More information
    Funded Activity

    Discovery Projects - Grant ID: DP180103232

    Funder
    Australian Research Council
    Funding Amount
    $387,884.00
    Summary
    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 more Read less
    More information
    Active Funded Activity

    Industrial Transformation Research Hubs - Grant ID: IH180100002

    Funder
    Australian Research Council
    Funding Amount
    $5,000,000.00
    Summary
    ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes .... ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.
    Read more Read less
    More information

    Showing 1-5 of 5 Funded Activites

    Advanced Search

    Advanced search on the Researcher index.

    Advanced search on the Funded Activity index.

    Advanced search on the Organisation index.

    National Collaborative Research Infrastructure Strategy

    The Australian Research Data Commons is enabled by NCRIS.

    ARDC CONNECT NEWSLETTER

    Subscribe to the ARDC Connect Newsletter to keep up-to-date with the latest digital research news, events, resources, career opportunities and more.

    Subscribe

    Quick Links

    • Home
    • About Research Link Australia
    • Product Roadmap
    • Documentation
    • Disclaimer
    • Contact ARDC

    We acknowledge and celebrate the First Australians on whose traditional lands we live and work, and we pay our respects to Elders past, present and emerging.

    Copyright © ARDC. ACN 633 798 857 Terms and Conditions Privacy Policy Accessibility Statement
    Top
    Quick Feedback