Visual sensing for localisation and mapping in mining. The creation of high quality survey data is integral to productivity and safety in mining and mining exploration. The current state-of-the-art mine surveying involves scanning from a number of fixed points using laser range-finding equipment (LIDAR). The aim of this project is to develop camera systems and computer vision algorithms to improve the speed and accuracy of this digital mapping of mines, to allow accurate mapping in locations den ....Visual sensing for localisation and mapping in mining. The creation of high quality survey data is integral to productivity and safety in mining and mining exploration. The current state-of-the-art mine surveying involves scanning from a number of fixed points using laser range-finding equipment (LIDAR). The aim of this project is to develop camera systems and computer vision algorithms to improve the speed and accuracy of this digital mapping of mines, to allow accurate mapping in locations denied GPS, and in locations where LIDAR cannot be deployed. The project aims to develop methods to assess these data to detect long-term trends such as shifts in mine drives which may be indicative of stress build-up. The new technology intends to impact both productivity and safety within mining.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
Scalable classification for massive datasets: randomised algorithms. The project will develop multi-class classification technologies capable of distinguishing between tens of thousands of classes, which are trained and applied to massive datasets. The technology will be applied in the field of microbiology, bringing high quality digital imaging and machine learning to this important area.
Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project aims to develop new techniques for identification of roadside obje ....Smart Information Processing for Roadside Fire Risk Assessment Using Computational Intelligence and Pattern Recognition. This project proposes a novel approach for identifying roadside fire risks using pattern recognition and computational intelligence techniques. The video data is collected over every state road in Queensland annually, and has the potential to provide a range of value-added products for safer roads. This project aims to develop new techniques for identification of roadside objects so that the data can be automatically analysed allowing the estimation of fire risk factors. The final outcome intends to be techniques for segmentation and classification of roadside objects and estimation of fire risk factors.Read moreRead less
Sentient buildings. This project aims to unite outputs from the large and varied array of sensors deployed in buildings into a coherent whole. By coordinating detections of resources and personnel from multiple sensors, it intends to enable more efficient allocation of shared resources within a public site such as a hospital, and enable a more effective emergency response. It intends to also allow the building to adapt over time to the way it is used, or to changing conditions. This is expected ....Sentient buildings. This project aims to unite outputs from the large and varied array of sensors deployed in buildings into a coherent whole. By coordinating detections of resources and personnel from multiple sensors, it intends to enable more efficient allocation of shared resources within a public site such as a hospital, and enable a more effective emergency response. It intends to also allow the building to adapt over time to the way it is used, or to changing conditions. This is expected to benefit the Australian construction industry as well as building operators, giving them a valuable export commodity. It intends also to benefit inhabitants of the buildings by providing a more safe, secure and accommodating environment.Read moreRead less
Precise recognition for automated harvesting and grading of strawberries. This project aims to improve automated strawberry harvesting to enable industrial harvesters to be deployed for commercial use and to lift the productivity of the Australian fruit industry. Precise recognition and grading of strawberries is a major obstacle in developing fully-automated commercial strawberry harvesting systems. Current colour-based fruit recognition techniques have intrinsic limitations in meeting the need ....Precise recognition for automated harvesting and grading of strawberries. This project aims to improve automated strawberry harvesting to enable industrial harvesters to be deployed for commercial use and to lift the productivity of the Australian fruit industry. Precise recognition and grading of strawberries is a major obstacle in developing fully-automated commercial strawberry harvesting systems. Current colour-based fruit recognition techniques have intrinsic limitations in meeting the needs of automatic strawberry harvesting. This project aims to investigate high-level syntactic recognition approaches that embed high-order texture patterns of ripe fruit and hyperspectral analysis techniques to achieve partially occluded fruit recognition and grading of fruit at the level required by commercial production.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.
Improving the face of cosmetic medicine - an automatic three-dimensional facial analysis system for facial rejuvenation. 'How will I look?' is the most common question to cosmetic doctors from patients considering facial rejuvenation. This project will answer this question for the first time by providing patients with a three-dimensional model of their post-treatment face as well as informing cosmetic doctors exactly how to achieve the patient's desired face.
Machine Learning for Fracture Risk Assessment from Simple Radiography. This project aims to develop a novel, reliable, low-cost system to detect poor bone health and assess fracture risk to help to prevent and manage osteoporosis-related fractures. Currently, osteoporosis-related fractures cost our health system millions of dollars annually and costs are increasing with our ageing population. Early detection of poor bone health will improve the effectiveness of preventive measures and ease this ....Machine Learning for Fracture Risk Assessment from Simple Radiography. This project aims to develop a novel, reliable, low-cost system to detect poor bone health and assess fracture risk to help to prevent and manage osteoporosis-related fractures. Currently, osteoporosis-related fractures cost our health system millions of dollars annually and costs are increasing with our ageing population. Early detection of poor bone health will improve the effectiveness of preventive measures and ease this burden. Current methods include unreliable, crude clinical and visual guides that suggest osteoporosis screening. The project plans to develop a novel system by applying machine learning algorithms to radiology data which is commonly captured for diagnosing other conditions.Read moreRead less
Robust and scalable change detection in geo-spatial data. A flood of data in the form of text, images and video emanate from a proliferation of sensors. These data are collected but rarely analysed, rendering it meaningless. This project aims to develop new software and techniques to detect changes over time in large scale geographically referenced data (for example photomaps) for use across numerous domains.