Robust face detection and recognition for computer-based security surveillance. The research aims at improving the existing and creating new automated face detection and recognition methods by making them invariant, firstly to head pose, orientation, scale and rotation, and then to occlusion, lighting conditions and facial expressions.
A robust face detector will be developed first and then a new face recognition algorithm that continues to learn identity-specific discriminants on-line by co ....Robust face detection and recognition for computer-based security surveillance. The research aims at improving the existing and creating new automated face detection and recognition methods by making them invariant, firstly to head pose, orientation, scale and rotation, and then to occlusion, lighting conditions and facial expressions.
A robust face detector will be developed first and then a new face recognition algorithm that continues to learn identity-specific discriminants on-line by collecting incremental face exemplars. The result of the research will be an algorithm that can improve its performance on-line adapting in a stable learning process each identity model to the correct facial examples.
The research has significant practical implication in visual surveillance increasing the robustness of identification of person identity, state and intent.
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Methodologies for automatic visual identification in heat detection aids. New techniques will be designed and developed to automate the existing manual heat detection of cattle, under general imaging conditions. The proposed intelligent system will consist of six stages: 1- image acquisition, 2- image preprocessing, 3- presence detection, 4- illumination compensation, 5- HD detection, and 6- heat detection. The proposed system will handle various image variations, and will be fast and cost-effec ....Methodologies for automatic visual identification in heat detection aids. New techniques will be designed and developed to automate the existing manual heat detection of cattle, under general imaging conditions. The proposed intelligent system will consist of six stages: 1- image acquisition, 2- image preprocessing, 3- presence detection, 4- illumination compensation, 5- HD detection, and 6- heat detection. The proposed system will handle various image variations, and will be fast and cost-effective. The developed system will improve the productivity of Australian cattle industry.Read moreRead less
Fruit shape estimation from stereoscopic images in real time. The research aims at improving the process of automatic fruit inspection and classification.
Existing stereo vision algorithms to extract depth information are unsuitable for real time calculations.
The increasing complexity and reducing cost of field programmable gate arrays along with the development of algorithms that have a high degree of parallelism and locality has created
the possibility of performing the calculation ....Fruit shape estimation from stereoscopic images in real time. The research aims at improving the process of automatic fruit inspection and classification.
Existing stereo vision algorithms to extract depth information are unsuitable for real time calculations.
The increasing complexity and reducing cost of field programmable gate arrays along with the development of algorithms that have a high degree of parallelism and locality has created
the possibility of performing the calculations required in real time.
This projects aims to investigate the suitability of the various stereo vision algorithms available in the literature for real time hardware implementation with application to fruit shape estimation it real time.
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Methodologies for face recognition under varying imaging conditions. Face recognition systems are heavily dependent on the nature of the input to the system. Variability in appearance due to changes in illumination, expression, pose, etc. can reduce the recognition results of the existing systems. The aim of this project is to develop new techniques to improve the recognition accuracy in natural environment where unwanted image variations exist. The development of such techniques will be of grea ....Methodologies for face recognition under varying imaging conditions. Face recognition systems are heavily dependent on the nature of the input to the system. Variability in appearance due to changes in illumination, expression, pose, etc. can reduce the recognition results of the existing systems. The aim of this project is to develop new techniques to improve the recognition accuracy in natural environment where unwanted image variations exist. The development of such techniques will be of great importance to Australia's security and safety. The outcome of this research will provide the first steps towards formulating the next generation recognition systems that will improve the suitability of the face recognition for use in security, surveillance, intelligent robotics, banking, and smart environments.Read moreRead less
Statistical Methods of Model Fitting and Segmentation in Computer Vision. Electronic sensors such as cameras and lasers can provide a rich source of information about the position, shape, and motion of objects around us. However, to extract this information in a reliable, automatic, and accurate way requires a sophisticated statistical theory of the process. Example applications include: video surveillance (better automatic detection of moving people and vehicles and of characterising what those ....Statistical Methods of Model Fitting and Segmentation in Computer Vision. Electronic sensors such as cameras and lasers can provide a rich source of information about the position, shape, and motion of objects around us. However, to extract this information in a reliable, automatic, and accurate way requires a sophisticated statistical theory of the process. Example applications include: video surveillance (better automatic detection of moving people and vehicles and of characterising what those people and vehicles are doing), industrial prototyping and inspection (measuring the size and shape of objects), urban planning (laser scanning streetscapes to create computer models of cities), entertainment industry (movie special effects and games), etc. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE120101778
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Building change detection and map update using multispectral imagery and height data. This project will produce an effective building change detection procedure and a digital building map. Automatic building detection assists in taking possible precautions during natural disasters, whilst automatic building change detection facilitates an effective and efficient management of affected areas during and after the calamity.
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.
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.
Surviving the data deluge: Scalable feature extraction, discrimination and analysis for computer vision tasks using compressed sensed data. Strategically, our pioneering solutions besides being technically and socially significant, open fresh options for sensor-agnostic data analysis. The technical significance lies through the creation of new technologies for the critical national and global security markets, currently overwhelmed by data. The social significance arises from our solutions being ....Surviving the data deluge: Scalable feature extraction, discrimination and analysis for computer vision tasks using compressed sensed data. Strategically, our pioneering solutions besides being technically and socially significant, open fresh options for sensor-agnostic data analysis. The technical significance lies through the creation of new technologies for the critical national and global security markets, currently overwhelmed by data. The social significance arises from our solutions being privacy preserving, providing new avenues for the production of novel, socially acceptable products for aged care monitoring. Our methods spearhead future advancement in diverse disciplines due to the wide applicability of the methods to other sensor networks (Square Kilometre Array) and data types, providing new frameworks for addressing crucial problems of data management. Read moreRead less