Feature-Level Fusion with Incomplete Data for Automatic Person Identification. This research addresses the current key problems in automated person recognition with incomplete data using multiple traits. The outcomes of this research will not only make a significant contribution to fundamental theory but also result in a wide range of crime and terrorism preventing applications including police database searching, access control, security monitoring and surveillance. They can be used either by p ....Feature-Level Fusion with Incomplete Data for Automatic Person Identification. This research addresses the current key problems in automated person recognition with incomplete data using multiple traits. The outcomes of this research will not only make a significant contribution to fundamental theory but also result in a wide range of crime and terrorism preventing applications including police database searching, access control, security monitoring and surveillance. They can be used either by police and law enforcement agencies, or at places of airport, government buildings, military facilities and even sensitive areas in offices and factories. It will help reduce crime, enhance the security of the nation to a world-advanced level, and generate new industry and export opportunities for Australian security industry.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
Face recognition under varying pose and lighting--towards automatic personal identification for surveillance systems. One of the key remaining problems in computerized human face recognition is the need to handle the variability in appearance due to changes in pose. This proposed research targets at identifying a person with a face image in a pose different from the example view by using a novel texture analysis and synthesis technique. This technique makes use of facial textures at different vi ....Face recognition under varying pose and lighting--towards automatic personal identification for surveillance systems. One of the key remaining problems in computerized human face recognition is the need to handle the variability in appearance due to changes in pose. This proposed research targets at identifying a person with a face image in a pose different from the example view by using a novel texture analysis and synthesis technique. This technique makes use of facial textures at different viewing directions and can recover appropriate textures for virtual views in arbitrary poses. The successfulness of the proposed research would make a technical breakthrough towards solving the major remaining problem in face recognition.Read moreRead less
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
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
Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcom ....Unlocking Mass Mobile Video Analytics with Advanced Neural Memory Networks. This project will develop neural memory architectures and dense spatial-temporal bundle adjustment to predict movement, behaviour, and perform multi-sensor fusion across large asynchronous video feeds. This capability will allow us to better interrogate and analyse mass video information recorded from the vast number of smartphones, action cameras, and surveillance cameras which exist at public events of interest. Outcomes include the ability to ingest multiple video feeds into a dense and dynamic 3D reconstruction for knowledge representation and discovery, and analysis of events and behaviour through new spatio-temporal analytic approaches. This will offer significant benefits for video forensic analysis, policing, and emergency response.Read moreRead less
Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features t ....Automated analysis of multi-modal medical data using deep belief networks. This project will develop an improved breast cancer computer-aided diagnosis (CAD) system that incorporates mammography, ultrasound and magnetic resonance imaging. This system will be based on recently developed deep learning techniques, which have the capacity to process multi-modal data in a unified and optimal manner. The advantage of this technique is that it is able to automatically learn both the relevant features to analyse in each modality and the hidden relationships between them. The use of deep belief networks has produced promising results in several fields, such as speech recognition, and so this project believes that our approach has the potential to improve both the sensitivity and specificity of breast cancer detection.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
Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps ....Human Cues for Robot Navigation. The world has many navigational cues for the benefit of humans: sign posts, maps and the wealth of information on the internet. Yet, to date, robotic navigation has made little use of this abundant symbolic information as a resource. This project will develop a robot navigation system that can navigate using information beyond the robot's range sensors by incorporating knowledge gained by reading room labels, following human route directions or interpreting maps found on the web. This project will demonstrate the robot's navigation ability by comparing its performance with a human as it learns to find its way around campus by asking for directions, reading signs and maps, and searching the internet for clues.Read moreRead less
Omniscient face recognition for uncooperative subjects. The outcomes of this project will enable effective video surveillance technology to be developed for use by law enforcement and national security agencies. It will lead to reliable identification of humans at a distance by automatically detecting and recognising faces, for use in counter-terrorism surveillance and commercial robot-human interfaces.