Target detection in visual clutter. The interdisciplinary nature of the project will offer a stimulating environment for training a postdoctoral worker in the hot topic of computational neuroscience. While computationally expensive solutions to moving target detection in clutter have been implemented using conventional engineering, this project will offer insight into the efficiency of the biological brain (with benefit of millions of years of evolution towards compact, economical and optimal so ....Target detection in visual clutter. The interdisciplinary nature of the project will offer a stimulating environment for training a postdoctoral worker in the hot topic of computational neuroscience. While computationally expensive solutions to moving target detection in clutter have been implemented using conventional engineering, this project will offer insight into the efficiency of the biological brain (with benefit of millions of years of evolution towards compact, economical and optimal solutions). The results will assist development of efficient artificial intelligence. It will also assist our ongoing collaborations with defence partners to develop and apply algorithms in artificial vision systems. Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less
Understanding and Modelling Insect Motion Vision. The interdisciplinary project proposed will offer a stimulating environment for research/training into computational neuroscience, an attractive area for aspiring scientists. We have already demonstrated the feasibility of transferring physiology into applications, and expect this project to deliver functional motion vision models and devices. Our proposed work will have an impact beyond the advancement of neuro-physiology as knowledge gained is ....Understanding and Modelling Insect Motion Vision. The interdisciplinary project proposed will offer a stimulating environment for research/training into computational neuroscience, an attractive area for aspiring scientists. We have already demonstrated the feasibility of transferring physiology into applications, and expect this project to deliver functional motion vision models and devices. Our proposed work will have an impact beyond the advancement of neuro-physiology as knowledge gained is applicable in a range of areas, with applications in miniature unmanned vehicles and collision avoidance detectors in defence and civilian roles. Our project could also assist in the development of artificial intelligence and as a basis for designing implantable artificial eyes.Read moreRead less
Visual intelligence for safe vehicle operation in industrial environment. Visual intelligence for safe vehicle operation in industrial environment. This project aims to develop safety devices for loosely constrained environments with public access, building on visual-based collision avoidance technology in controlled industrial settings. Increasing productivity in industrial workplaces creates a need for faster industrial vehicles. At fruit and vegetable markets and construction sites, forklift ....Visual intelligence for safe vehicle operation in industrial environment. Visual intelligence for safe vehicle operation in industrial environment. This project aims to develop safety devices for loosely constrained environments with public access, building on visual-based collision avoidance technology in controlled industrial settings. Increasing productivity in industrial workplaces creates a need for faster industrial vehicles. At fruit and vegetable markets and construction sites, forklift drivers, crane operators and crews are under pressure to move faster. The need for higher speed and the enormous human and financial cost of unsafe operations create opportunities for the deployment of intelligent safety devices. The expected outcomes of this project are safer public industrial environments, reductions in work related injuries, injury compensation costs and associated societal burdens.Read moreRead less
Intelligent collision avoidance system for mobile industrial platforms. This project will develop a collision prevention system for mobile industrial platforms that enhances existing artificial vision perception systems to mimic human eye capabilities. The outcomes of this project will result in significant reductions in work related injuries, injury compensation costs and associated societal burdens.
Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will desig ....Deep Learning that Scales. Deep learning has dramatically improved the accuracy of a breathtaking variety of tasks in AI such as image understanding and natural language processing. This project addresses fundamental bottlenecks when attempting to develop deep learning applications at scale. First, this project proposes efficient neural architecture search that is orders of magnitude faster than previously reported, abstracting away the most complex part of deep learning. Second, we will design very efficient binary networks, enabling large-scale deployment of deep learning to mobile devices. Thus this project will overcome two primary limitations of deep learning generally, however, and will greatly increase its already impressive domain of practical application.Read moreRead less
Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collabor ....Adapting Deep Learning for Real-world Medical Image Datasets. The project aims to investigate new deep learning modelling approaches to leverage real-world large-scale image data sets that contain noisy and incomplete labels and imbalanced class prevalence – to enable the use of these data sets for modelling deep learning classifiers. Expected outcomes include an innovative method for modelling deep learning classifiers. The research will involve new inter-disciplinary and international collaborations with machine learning and medical image analysis research institutions. This should provide significant benefits, such as better understanding of deep learning theory, new deep learning applications that can use previously unexplored data sets, and training for the future Australian workforce.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
Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the ....Deep reinforcement learning for discovering and visualising biomarkers. This project aims to develop novel methods for discovering and visualising optimal bio-markers from chest computed tomography images based on extensions of recently developed deep reinforcement learning techniques. The extensions proposed in this project will advance medical image analysis by allowing an efficient analysis of large dimensionality inputs in their original high resolution. In addition, this project will be the first approach capable of discovering previously unknown biomarkers associated with important clinical outcomes. The project will validate the approach on a real-world case study data set concerning the prediction of five-year survival of chronic disease.Read moreRead less
New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project wil ....New Paradigms for Robust Fitting: Kernelisation and Polyhedral Search. Outliers inevitably exist in visual data due to imperfect data acquisition or preprocessing. To enable computer vision applications that can perform reliably, robust fitting algorithms are necessary to counter the biasing influence of outliers. However, current robust algorithms are unsatisfactory: they are unreliable (due to using randomisation) or too computationally costly (due to using exhaustive search). This project will develop new robust algorithms to mitigate these shortcomings. It will do so by investigating two new paradigms of kernelisation and polyhedral search, which offer unprecedented theoretical insights into the problem. The outcomes will contribute towards computer vision applications that are more practical and reliable.Read moreRead less