Audio Visual Speech Recognition. Even though significant advances have been made in automatic speech recognition using acoustic information, the recognition accuracies are still poor in noisy and hostile environments such as in crowds, traffic, factory floors etc. In many of these applications visual information is or can easily be made available in addition to the audio. The aim of this project is to achieve an order of magnitude improvement in speech recognition accuracies in adverse environme ....Audio Visual Speech Recognition. Even though significant advances have been made in automatic speech recognition using acoustic information, the recognition accuracies are still poor in noisy and hostile environments such as in crowds, traffic, factory floors etc. In many of these applications visual information is or can easily be made available in addition to the audio. The aim of this project is to achieve an order of magnitude improvement in speech recognition accuracies in adverse environments by joint processing and modelling of the acoustic modality with visual information in the form of lip shapes and movements. The outcomes will be useful in human computer interaction in adverse environments as well as in the transcription and mining of multimedia data.
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Improved detection and characterisation of breast cancer using magnetic resonance imaging, and novel image analysis and pattern recognition techniques. Breast cancer is a leading cause of death in Australian women. With no clear cause, one mainstay of management has been early detection. Newer medical imaging technologies such as magnetic resonance imaging require complex analysis to achieve their full benefit. Should the computationally demanding analyses of these images provide more sensitive ....Improved detection and characterisation of breast cancer using magnetic resonance imaging, and novel image analysis and pattern recognition techniques. Breast cancer is a leading cause of death in Australian women. With no clear cause, one mainstay of management has been early detection. Newer medical imaging technologies such as magnetic resonance imaging require complex analysis to achieve their full benefit. Should the computationally demanding analyses of these images provide more sensitive and specific detection of early cancers, the potential reductions in morbidity and mortality from breast cancer will be of immense value. Successful implementation of the proposed project will further enhance Australia's position as a world leader in biomedical research and application of computational technologies to health problems.Read moreRead less
Special Research Initiatives - Grant ID: SR0567196
Funder
Australian Research Council
Funding Amount
$55,000.00
Summary
Improved early detection of breast cancer enabled by grid-computing and advanced modelling and visualisation of MR images. This project will investigate the utility of grid computing in the detection of breast cancer from magnetic resonance (MR) images. The large quantity of data acquired using MR imaging is difficult for clinicians to review and the cost of missed or incorrect detection is high. To provide rapid visualisation and assessment of the acquired data, grid computing will be used in c ....Improved early detection of breast cancer enabled by grid-computing and advanced modelling and visualisation of MR images. This project will investigate the utility of grid computing in the detection of breast cancer from magnetic resonance (MR) images. The large quantity of data acquired using MR imaging is difficult for clinicians to review and the cost of missed or incorrect detection is high. To provide rapid visualisation and assessment of the acquired data, grid computing will be used in conjunction with interactive visualisation with haptic feedback. Grid computing experience and haptic device expertise will be achieved via Swedish collaborators. The successful outcome of this project will be software for the production of 3D colour-coded breast images in which suspicious regions are highlighted and can be physically interrogated using the haptic device.Read moreRead less
Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discove ....Unsupervised learning of finite mixture models in data mining applications. The extraction of useful information from massively large databases is known as data mining. Its broad but vague goal is to find "interesting structure" in the data, which typically leads to breaking the data into clusters. To this end, we consider the fast, efficient, and automatic learning of finite mixture models in hugh data sets without any prior knowledge of the structure. This probabilistic approach to the discovery and validation of group structure in data mining applications will considerably enhance knowledge management and decision support in science, industry, and government.
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