Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior ....Pattern Recognition and Scene Analysis via Machine Learning. We plan to use kernel methods, a novel machine learning technique, for computer vision problems, such as scene analysis and real time object recognition. Such capabilities are relevant for the design of intelligent and adaptive systems, suitable for complex real world environments. Expected outcomes are the design of efficient statistical tools which take the special nature of visual data into account (structure, decomposition, prior knowledge of physical environments, etc.) and combine the advantages of feature based high-level vision methods with low-level machine learning techniques.
This proposal is part of a joint IST project with partners from the European Union.Read moreRead less
Learning clique potentials for high-order graphical models. This project aims to develop algorithms for computers to automatically learn about visual scenes and objects from images. Using our algorithms, computers will be able to find objects and describe scenes in single images or large image collections such as online photo albums.
Autonomous Functions for Smart Cars. The aim of this project is to develop autonomous functions for smart cars, such as lane departure warning, driver fatigue warning, and automatic lane following. Every year 70,000 people are killed in road accidents, 95% of which can be attributed to driver error. The potential outcomes of this project therefore significant. Many of the theoretical methods required for this project have been developed by our group. However, further theoretical refinements fo ....Autonomous Functions for Smart Cars. The aim of this project is to develop autonomous functions for smart cars, such as lane departure warning, driver fatigue warning, and automatic lane following. Every year 70,000 people are killed in road accidents, 95% of which can be attributed to driver error. The potential outcomes of this project therefore significant. Many of the theoretical methods required for this project have been developed by our group. However, further theoretical refinements followed by experimental verification is necessary. For smart cars to be accepted, the systems must be demonstrated to be reliable and to operate in a wide range of conditions.Read moreRead less
Added depth: automated high level image interpretation. Humans are very good at understanding the world through imagery, but computers lack this fundamental capacity because they lack experience of what they might see. This project will provide this experience by combining the large volumes of imagery on the Internet with three dimensional information generated by humans for other purposes.
Structure-without-motion: large-scale 3D reconstruction from distributed and unorganised images. Vision-based 3D reconstruction is a frontier technology for a wide range of applications. This project will lead to novel 3D reconstruction methods and systems that are more efficient, more cost-effective and more accessible to ordinary user. The outcomes will directly contribute to National Research Priority Goal of smart information use.
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
Lifelong robotic navigation using visual perception. Service robots are becoming a major part of our working and personal environments, in much the same way as personal computers already have. This project will develop new methods of practical and useful robot navigation that will enable Australia's industries and services to remain internationally competitive.
Enhanced ultrasound-based imaging using image-based registration and acoustic impedance reconstruction. The project will nurture the development of a new centre for medical image analysis work in Australia at the ANU. This is in line with the vision of ANU's Department of Engineering for the growth of biomedical engineering research. The project is directed at the creation of new surgical and imaging techniques based on ultrasound. These will have a direct effect on improved healthcare and new c ....Enhanced ultrasound-based imaging using image-based registration and acoustic impedance reconstruction. The project will nurture the development of a new centre for medical image analysis work in Australia at the ANU. This is in line with the vision of ANU's Department of Engineering for the growth of biomedical engineering research. The project is directed at the creation of new surgical and imaging techniques based on ultrasound. These will have a direct effect on improved healthcare and new clinical procedures. The creation of a new ultrasound imaging modality will have commercial applications, enhancing the growth of biomedical engineering in Australia. The training of new PhD students and postdoctoral fellows will provide a basis for further development in this area, and its extension to other imaging research in Australia. Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100156
Funder
Australian Research Council
Funding Amount
$210,000.00
Summary
Computational infrastructure for machine learning in computer vision. The many trillions of images stored on computers around the world, including more than 100 billion on Facebook alone, represent exactly the information needed to develop artificial vision. All we need do is extract it. This project will develop the computational infrastructure required to allow Australian researchers to achieve this goal.