Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vecto ....Semantic Vectorisation: From Bitmaps to Intelligent Representations. The objective of this innovative project is to provide a solution to the open question of representing natural images by semantically rich vector graphics. The challenges are to identify key visual and temporal elements for images and videos, and efficiently decompose the visual data into semantic vector representations that are faithful to original data, compact and editable. The project aims to investigate new bitmap-to-vector conversion methods. It is expected to develop a framework where semantic labels and hyperlinks can be embedded in visual data automatically. It hopes to pioneer the creation of a web of images where the links are on image/video regions. New image simplification, stylisation, and non-photorealistic rendering methods are expected to be provided.Read moreRead less
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
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