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Computer vision techniques for forensic engineering and damage assessment in structures

This research will develop high-end technology based on computer vision for structural health monitoring and damage localisation in structures. more...

Supervisor(s): Dias-da-Costa, Daniel (Dr)

Medical Image Analysis with Machine Learning Techniques

Develop artificial intelligence for computer-assisted diagnosis from medical scans. more...

Supervisor(s): Zhou, Luping (Dr)

Biometrics (face recognition, human gait recognition or person re-identification)

This research project aims to develop new feature extraction and classification methods for face recognition, human gait recognition or person re-identification. more...

Supervisor(s): Xu, Dong (Professor)

Semantic-driven Multi-modal Biomedical Data Visualisation

BMIT excels at addressing bio-inspired and other real-world challenges with core computing and information technology research in image processing and informatics, computer vision, more...

Supervisor(s): Kim, Jinman (Associate Professor), Feng, David (Professor)

Video analytics (activity and video event recognition)

This research project aims to develop new intelligent video analytics systems for understanding videos. more...

Supervisor(s): Xu, Dong (Professor)

Machine learning in Multiscale Image-Omics

BMIT excels at addressing bio-inspired and other real-world challenges with core computing and information technology research in image processing and informatics, computer vision, more...

Supervisor(s): Feng, David (Professor), Kim, Jinman (Associate Professor)

Disease Map - Big Data driven modelling and derivation of diseases and treatment response

BMIT excels at addressing bio-inspired and other real-world challenges with core computing and information technology research in image processing and informatics, computer vision, more...

Supervisor(s): Feng, David (Professor), Kim, Jinman (Associate Professor)

Bayesian deep learning for incomplete information

This project will feature a synergy of deep learning, modular and multi-task learning with Bayesian methods to address the problem of decision making given incomplete information. more...

Supervisor(s): Cripps, Sally (Professor), Chandra, Rohitash (Dr)

Advanced orchard mapping systems using robotics, sensing and perception

The aim of the project is to research and develop intelligent orchard mapping systems that provide timely, high resolution data to support farm management. more...

Supervisor(s): Underwood, James (Dr)

New technologies for detecting, counting and identifying pollinators in the field

This project will leverage advances in image processing and machine learning to develop new techniques for high throughput sampling of pollinator populations in crops.  Th more...

Supervisor(s): Latty, Tanya (Dr)