Interactive and scalable media over software defined networks. A novel API and associated algorithms will be developed to exploit the emerging technology of software defined networks (SDN) for improving the efficiency and responsiveness of interactive media browsing applications. The approach applies to conventional streaming video as well as more interactive services based on scalable media compression and communication technology, notably JPIP (IS15444-9) video. Recent advances in motion codin ....Interactive and scalable media over software defined networks. A novel API and associated algorithms will be developed to exploit the emerging technology of software defined networks (SDN) for improving the efficiency and responsiveness of interactive media browsing applications. The approach applies to conventional streaming video as well as more interactive services based on scalable media compression and communication technology, notably JPIP (IS15444-9) video. Recent advances in motion coding will be combined with new spatio-temporal transforms to develop an efficient inter-frame extension to the JPEG 2000 standard that is fully compatible with JPIP. Each of these innovations is important in its own right, but together they will facilitate a highly compelling interactive media browsing experience.Read moreRead less
Innovations In Cancer Imaging And Targeted Radiotherapy To Improve Human Health
Funder
National Health and Medical Research Council
Funding Amount
$926,980.00
Summary
Through a process of discovery, development and investigation we will create medical devices and methods to improve cancer imaging and targeted radiotherapy. Successful completion of this program will directly impact on the treatment and lives of Australian cancer patients in the foreseeable future.This program will substantially build research capacity and productivity within Australia, raise Australia’s profile in cancer research and foster international collaboration.
Compression and communication of single and multi-view video based on overlapping motion hint fields. This project explores a new way of communicating motion for video and multi-view (3D) applications, facilitating efficient interactive access to content. Outcomes will include new compression methods that avoid redundant transmission of motion side information, plus client/server technology that leverages metadata from smart surveillance cameras.
Efficient multi-view video coding with cuboids and base anchored models. This project aims to address current deficiencies in multi-view video coding technology to achieve the ultra-compression efficiency demanded by increasing display resolutions and synchronised viewpoints. The project expects to generate new knowledge, by moving from the current pixel-centric approach to methods that concentrate information common to many view-frames. The project is expected to improve compression of audio-vi ....Efficient multi-view video coding with cuboids and base anchored models. This project aims to address current deficiencies in multi-view video coding technology to achieve the ultra-compression efficiency demanded by increasing display resolutions and synchronised viewpoints. The project expects to generate new knowledge, by moving from the current pixel-centric approach to methods that concentrate information common to many view-frames. The project is expected to improve compression of audio-visual services that are of great interest to international standards bodies and industry, while facilitating free interaction and augmented reality. This project will provide significant benefits to broadcast, entertainment, surveillance and health industries and position Australia as a world leader in this field.Read moreRead less
Video plasticity: Scalable video coding with inherently consistent motion. This project aims to improve how video coders represent motion, leading to more efficient motion descriptions and fewer distinct motion fields. The project will develop motion inference algorithms that ensure consistent motion descriptions throughout a group of pictures, allowing seamless integration of scalable video coding, motion compensated temporal filtering and motion compensated frame interpolation operations. The ....Video plasticity: Scalable video coding with inherently consistent motion. This project aims to improve how video coders represent motion, leading to more efficient motion descriptions and fewer distinct motion fields. The project will develop motion inference algorithms that ensure consistent motion descriptions throughout a group of pictures, allowing seamless integration of scalable video coding, motion compensated temporal filtering and motion compensated frame interpolation operations. The project is expected to support an efficient and interactive video browsing experience, largely decoupled from original frame rate and resolution; and deliver practical solutions that can be efficiently implemented on consumer devices.Read moreRead less
A general Bayesian multilinear analysis framework for human behaviour recognition. Smart information use is essential for effective video surveillance in order to guard against accidents, fight crime and combat terrorism. In this project advanced probabilistic methods will be applied to visual surveillance information, to warn of impending accidents and to track criminals and terrorists and predict their behaviours.
Discovery Early Career Researcher Award - Grant ID: DE180101438
Funder
Australian Research Council
Funding Amount
$356,446.00
Summary
Multi-view synergistic learning for human behaviour analysis. This project aims to equip machines with a human-likeability to synergistically harness multiple information sources for the purpose of optimal decision-making. This project will produce the next great step for machine intelligence - laying the theoretical foundation for the learning of multiple views and building the next generation of intelligent systems which can accommodate multiple information sources. This research is fundament ....Multi-view synergistic learning for human behaviour analysis. This project aims to equip machines with a human-likeability to synergistically harness multiple information sources for the purpose of optimal decision-making. This project will produce the next great step for machine intelligence - laying the theoretical foundation for the learning of multiple views and building the next generation of intelligent systems which can accommodate multiple information sources. This research is fundamental to the creation of intelligent systems that elegantly tackle varieties of big data. This should benefit science, society, and the economy nationally through applications including autonomous vehicle development, sensor technologies, and human behaviour analysis.Read moreRead less
Nonlinear Transfer Distance Metric Learning for Gleaning Knowledge from the Crowd. This project will develop nonlinear transfer distance metric learning algorithms for training and test samples that are not independent and identically distributed, or from different instance spaces. New theoretical foundations for crowd-sourcing will lead to innovative intelligent systems for such purposes as the NBN, social, and security services, and keep pace with developments in hardware technology. The outco ....Nonlinear Transfer Distance Metric Learning for Gleaning Knowledge from the Crowd. This project will develop nonlinear transfer distance metric learning algorithms for training and test samples that are not independent and identically distributed, or from different instance spaces. New theoretical foundations for crowd-sourcing will lead to innovative intelligent systems for such purposes as the NBN, social, and security services, and keep pace with developments in hardware technology. The outcomes include applications in social networks, the Internet, and climate change, as well as video surveillance to help combat crime and terrorism. The innovative research will significantly benefit Australia’s economy, environment and society, and will maintain Australia's global leading role in the machine learning and computer vision.Read moreRead less
Streaming label learning for leaching knowledge from labels on the fly. This machine intelligence project aims to explore the potential to use and incorporate past knowledge and training to better understand, interpret and develop new concepts. The expected outcomes will provide major technological breakthroughs to benefit science, society, and the economy nationally by laying theoretical foundations for learning labels in a streaming fashion, and building the next generation of intelligent syst ....Streaming label learning for leaching knowledge from labels on the fly. This machine intelligence project aims to explore the potential to use and incorporate past knowledge and training to better understand, interpret and develop new concepts. The expected outcomes will provide major technological breakthroughs to benefit science, society, and the economy nationally by laying theoretical foundations for learning labels in a streaming fashion, and building the next generation of intelligent systems to accommodate environment change in applications about cybercrime, terrorism, and emergence.Read moreRead less
A probabilistic framework for nonlinear dimensionality reduction algorithms. The Twin Measures Framework is a novel platform for analysing existing dimensionality reduction methods and the invention of new ones. This research will radically improve image analysis, with beneficial applications from pharmaceutical drug design through to border protection.