Learning to see in 3D. The project aims to endow machine vision with an ability we, as humans, use almost constantly: to judge 3D properties from a 2D image. This extremely useful ability will be applied to digital images to obtain 3D measurements and aid in automating tasks such as mining, surveying, medical diagnosis, and visual effects in movies.
Recognising and reconstructing objects in real time from a moving camera. This project will use a moving camera to estimate the three-dimensional shape and identity of objects and surfaces it can see. This ability, which we humans use all the time, has wide application in automation including driver assistance, exploring hazardous environments, robotics, remote collaboration, and the creation of three-dimensional models for entertainment.
Optimal Robust Fitting under the Framework of LP-Type Problems. The project aims to develop algorithms to support the development of robust and accurate computer vision systems. Real-world visual data (images, videos) is inherently noisy and outlier prone. To build computer vision systems that work reliably in the real world, it is necessary to ensure that the underlying algorithms are robust and efficient. The project aims to devise novel algorithms that can compute the best possible result giv ....Optimal Robust Fitting under the Framework of LP-Type Problems. The project aims to develop algorithms to support the development of robust and accurate computer vision systems. Real-world visual data (images, videos) is inherently noisy and outlier prone. To build computer vision systems that work reliably in the real world, it is necessary to ensure that the underlying algorithms are robust and efficient. The project aims to devise novel algorithms that can compute the best possible result given the input data in a short amount of time. The expected outcomes would support the construction of reliable and accurate computer vision-based systems, such as large-scale 3-D reconstruction from photo collections, self-driving cars and domestic robots.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL130100102
Funder
Australian Research Council
Funding Amount
$3,179,946.00
Summary
Lifelong computer vision systems. This project will create a computer vision system that can produce a detailed environmental map in real time, turning standard video cameras into sensors that 'understand' a scene with basic semantic tools. This high-level sensing will unlock a wide range of applications for autonomous systems.
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
Fusion of digital microscopy and plain text reports for automated analysis. The project aims to develop advanced computer-aided analytics systems with the goal to improve the workflow and automation in the pathology industry. Improvements will be achieved by fusing information from both digital images and plain text medical reports. In collaboration with a partner organisation, the project team will field trial the new analytics systems against traditional pathology tests to evaluate both effica ....Fusion of digital microscopy and plain text reports for automated analysis. The project aims to develop advanced computer-aided analytics systems with the goal to improve the workflow and automation in the pathology industry. Improvements will be achieved by fusing information from both digital images and plain text medical reports. In collaboration with a partner organisation, the project team will field trial the new analytics systems against traditional pathology tests to evaluate both efficacy and reliability. In addition, the project is also aimed to construct a large digital slide databank which will aid training and education. The expected outcome of the project is to perform existing tasks cheaper and more efficiently. Read moreRead less
Visual tracking with environmental constraints. By incorporating high level scene understanding into visual tracking, this project will improve the capacity to monitor and analyse complex patterns of activity in video. This has many applications in public safety and security, but the project will demonstrate it on the challenging task of tracking players during an Australian Football League (AFL) game to gather statistics on their performance.
Autocalibration without decimation. The insertion of computer generated characters into real footage, the removal of objects from video, and the recovery of 3-dimensional architectural or topographic models from photographs are amongst a growing number of processes used in industry which require highly accurate camera calibration. Autocalibration is thus a prerequisite for these and many other emerging image-based technologies. By developing expertise in this area, and particularly by enabling ....Autocalibration without decimation. The insertion of computer generated characters into real footage, the removal of objects from video, and the recovery of 3-dimensional architectural or topographic models from photographs are amongst a growing number of processes used in industry which require highly accurate camera calibration. Autocalibration is thus a prerequisite for these and many other emerging image-based technologies. By developing expertise in this area, and particularly by enabling more flexible and efficient means of autocalibration, we expect to provide Australian industry with a valuable improvement in the state of the art and a competitive edge in a number of important application areas.Read moreRead less
Combined shape and appearance descriptors for visual object recognition. The quantity of video generated each year is expanding rapidly. This increasing volume of visual information means that it is more likely that any particular event will be recorded, but that the footage will be harder to find. This applies to a collection of home videos as much as to television and movie footage. The object-recognition method to be developed has the potential to alleviate this situation, in which vast amou ....Combined shape and appearance descriptors for visual object recognition. The quantity of video generated each year is expanding rapidly. This increasing volume of visual information means that it is more likely that any particular event will be recorded, but that the footage will be harder to find. This applies to a collection of home videos as much as to television and movie footage. The object-recognition method to be developed has the potential to alleviate this situation, in which vast amounts of video data are available but have little value. Such an outcome would be a boon for Australian industry and offer a valuable export opportunity.Read moreRead less
Multi-objective parameter estimation techniques for computer vision. This project will benefit Australia's scientific knowledge and technology base in the area of computer vision. By contributing improved methods for parameter estimation applicable to a wide variety of technical problems, the project will aid the generation of improved software products in a wide variety of domains. Examples include: augmented reality systems, with which virtual reality artifacts may be immersed within real vid ....Multi-objective parameter estimation techniques for computer vision. This project will benefit Australia's scientific knowledge and technology base in the area of computer vision. By contributing improved methods for parameter estimation applicable to a wide variety of technical problems, the project will aid the generation of improved software products in a wide variety of domains. Examples include: augmented reality systems, with which virtual reality artifacts may be immersed within real video; 3D from 2D systems, with which 3D object structure may be computed from image streams; and visual robotic systems, with which the pose of viewed objects may be determined.Read moreRead less