Discovery Early Career Researcher Award - Grant ID: DE140100180
Funder
Australian Research Council
Funding Amount
$394,305.00
Summary
Advancing Dense 3D Reconstruction of Non-rigid Scenes by Using a Moving Camera. This project will advance the fundamental research in geometric computer vision and develop a new framework for efficient dense three-dimensional reconstruction of non-rigid scenes by using a moving camera. It is expected that this project will bring about breakthroughs in geometric computer vision with many daily applications, including three-dimensional natural human-computer interaction, three-dimensional reconstr ....Advancing Dense 3D Reconstruction of Non-rigid Scenes by Using a Moving Camera. This project will advance the fundamental research in geometric computer vision and develop a new framework for efficient dense three-dimensional reconstruction of non-rigid scenes by using a moving camera. It is expected that this project will bring about breakthroughs in geometric computer vision with many daily applications, including three-dimensional natural human-computer interaction, three-dimensional reconstruction from historical movies and three-dimensional realistic animations. Its outcomes will enable users to capture and manipulate their surrounding dynamic world in three-dimensions easily and conveniently. This project will alleviate many of the major difficulties (dense correspondences, long sequences, complex deformations) with conventional non-rigid reconstruction methods.Read moreRead less
Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive. Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than ai ....Solve it or Ignore it? The Challenge of Alignment Distortion and Creating Next Generation Automatic Facial Expression Detection. The last two decades have seen an escalating interest in automating the coding of facial expressions. Despite this keen interest, the promise of computer vision systems to accurately code facial expressions in natural circumstances remains elusive. Our interdisciplinary team will research a new paradigm to account for facial alignment distortion directly rather than aiming to achieve invariance to it. The project will also research new data agnostic feature compaction capabilities to enable scalable learning on the world’s largest and challenging expression dataset available to us through international collaboration. Tackling these two major open problems will make accurate coding of facial expressions in natural environments achievable.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
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.
Discovery Early Career Researcher Award - Grant ID: DE160100241
Funder
Australian Research Council
Funding Amount
$300,000.00
Summary
Learning Network Structures from Neuroimages for Diagnosing Brain Diseases. This project aims to develop a probabilistic inference framework based on graphical models to enable discriminative, interpretable and reliable analysis of brain imaging data. Recent development of computer-assisted neuroimage analysis calls for advanced pattern recognition methods. To meet this requirement, this project proposes a framework that addresses several critical issues in this process, and to provide important ....Learning Network Structures from Neuroimages for Diagnosing Brain Diseases. This project aims to develop a probabilistic inference framework based on graphical models to enable discriminative, interpretable and reliable analysis of brain imaging data. Recent development of computer-assisted neuroimage analysis calls for advanced pattern recognition methods. To meet this requirement, this project proposes a framework that addresses several critical issues in this process, and to provide important models and algorithms to identify brain connectivity patterns and benefit the diagnosis of diseases. The output of this project is expected to include a set of effective computational algorithms and computer-assisted tools, which can help medical researchers to identify brain disorders with better precision, repeatability and objectivity.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.
Dynamic Visual Scene Gist Recognition using a Probabilistic Inference Framework. How can we see the forest without intentionally looking for the trees? How can we tell traffic is flowing smoothly on a busy highway without identifying vehicles or measuring their speed? These are the questions that inspire this research project. Humans are endowed with the ability to grasp the ‘gist’ or overall meaning of a complex visual scene from a single glance and without attention to details. The aim of this ....Dynamic Visual Scene Gist Recognition using a Probabilistic Inference Framework. How can we see the forest without intentionally looking for the trees? How can we tell traffic is flowing smoothly on a busy highway without identifying vehicles or measuring their speed? These are the questions that inspire this research project. Humans are endowed with the ability to grasp the ‘gist’ or overall meaning of a complex visual scene from a single glance and without attention to details. The aim of this project is to develop new computational vision models that combine biological visual processing with probabilistic inference for gist recognition. The developed models will be able to mimic human vision by analysing a complex dynamic scene rapidly and classifying its semantic categories, without identifying individual objects.Read moreRead less
The cellular basis of branching morphogenesis during kidney development. This project aims to study the process of branching morphogenesis which drives the development of the kidney. Previous studies group have demonstrated, in general terms, how branching progresses during gestation. However, little is known about the fundamental cellular events which trigger or characterise this basic developmental process. This project expects to provide deep insights into the cellular basis of tissue and org ....The cellular basis of branching morphogenesis during kidney development. This project aims to study the process of branching morphogenesis which drives the development of the kidney. Previous studies group have demonstrated, in general terms, how branching progresses during gestation. However, little is known about the fundamental cellular events which trigger or characterise this basic developmental process. This project expects to provide deep insights into the cellular basis of tissue and organ development. In studying this process the project should provide critical insights into how cells act, individually and collectively, to build tissues.Read moreRead less
Multi-modal virtual microscopy for quantitative diagnostic pathology. This project will contribute to the next generation of virtual microscopy systems that provide innovative features capable of significantly increasing the adoption of digital imaging technology throughout the field of diagnostic pathology. These tools will especially contribute to the screening and diagnosis of cervical, lung and bladder cancer.
Field and quasi-field phenotyping for the quantitative characterisation of wheat yield under stress. The project aims to develop state-of-the-art monitoring and profiling capabilities for the quantitative assessment of plant growth performance in field and quasi-field environments under the abiotic stress conditions of drought and nutrient deficiency. This project involves the design and use of high resolution but low budget imaging stations to capture the growth of cereal plants in competitive ....Field and quasi-field phenotyping for the quantitative characterisation of wheat yield under stress. The project aims to develop state-of-the-art monitoring and profiling capabilities for the quantitative assessment of plant growth performance in field and quasi-field environments under the abiotic stress conditions of drought and nutrient deficiency. This project involves the design and use of high resolution but low budget imaging stations to capture the growth of cereal plants in competitive environments. Novel computer vision and image processing techniques will be applied to the image data to quantitatively characterise the success of genetic varieties to tolerate abiotic stress environments under actual field conditions.Read moreRead less