Space-based space surveillance with robust computer vision algorithms. Space-based space surveillance with robust computer vision algorithms. This project aims to develop computer vision algorithms to detect man-made objects in space. These algorithms function on nanosatellite platforms, enabling space-based space surveillance. This technology is expected to provide always-on monitoring of the Earth's orbit to enhance existing defence infrastructure and protect vital space assets, including comm ....Space-based space surveillance with robust computer vision algorithms. Space-based space surveillance with robust computer vision algorithms. This project aims to develop computer vision algorithms to detect man-made objects in space. These algorithms function on nanosatellite platforms, enabling space-based space surveillance. This technology is expected to provide always-on monitoring of the Earth's orbit to enhance existing defence infrastructure and protect vital space assets, including communications and navigational satellites, in Earth’s orbit from collisions and covert sabotage. Increased space use by government and civilian agencies opens up opportunities for the space industry. This project is expected to develop Australia’s space surveillance capabilities, protect space assets and capture a growing market.Read moreRead less
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
A theoretical framework for practical partial fingerprint identification. Fingerprints captured from a crime scene are often partial and poor quality which makes it difficult to identify the criminal suspects from large databases. This project will find mathematical models which can estimate the missing information located in the blank areas of a partial fingerprint and effectively identify it.