Research Grant
[Cite as https://purl.org/au-research/grants/arc/DP160103490]Researchers: David Suter (Chief Investigator) , Prof Tat-Jun Chin (Chief Investigator)
Brief description 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.
Funding Amount $268,000
Funding Scheme Discovery Projects
- PURL : https://purl.org/au-research/grants/arc/DP160103490
- ARC : DP160103490