In today’s world of cutting-edge technologies, the acquisition of instant information is possible through advancements in digital image capture and processing. Image processing refers to the recognition, manipulation and image enhancement of a digital image to achieve an aesthetic quality, or to derive data to support another task. From detection systems at traffic lights to fingerprint and biometric security systems, digital imaging is crucial to a wide variety of real-world applications where vision is involved.
Image processing techniques are already used within a diverse range of industries and hold huge future potential. Image processing algorithms and artificial intelligence are now so accurate they can be used to sort items in a manufacturing assembly line, diagnose defects in the human body, shape our knowledge of climate change through satellite data and identify enemy objects in combat.
We’re developing novel theories and algorithms to capture, process, analyse and understand single and multiple images and videos. Our research covers a diverse range of areas including:
Our research has been instrumental in the development of a new multi-modal similarity measure called the Sum of Conditional Variance (SCV). The SCV is the first information-theoretic similarity measure that can be optimised using standard optimisation techniques. The SCV similarity measure is adopted for use by leading international research teams including:
We have world-leading expertise in:
Our collaboration with the Trauma and Orthopaedic Research Unit (TORU) at the Canberra Hospital since 2005 has developed more effective methods to measure the relative motion of the bones in human joints using standard hospital imaging equipment. The major outcome of this project is the development of a software package called OrthoVis.
The algorithms used in OrthoVis are a result of several PhD student projects supervised by members of the Imaging group. This software is now an intrinsic part of a major clinical trial to evaluate the performance of three different artificial knee designs.
We also demonstrate the applications of remote sensing imagery across:
Xu M, Jia X, Pickering M, Jia S, Thin cloud removal from optical remote sensing images using the noise-adjusted principal components transform, ISPRS Journal of Photogrammetry and Remote Sensing 149:215-225 01 Mar 2019
Y. Guo, X. Jia and D. Paull, “Effective sequential classifier training for SVM-based multitemporal remote sensing image classification,” Transactions on Image Processing, 27 (6): 3036 – 3048, 2018.
Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi, “Deep feature extraction and classification of hyperspectral images based on Convolutional Neural Networks,” IEEE Trans. Geosci. Remote Sens., 54(10): 6232 - 6251, 2016.
X. Jia, B.-C. Kuo, and M. Crawford, "Feature mining for hyperspectral image classification," Proceedings of the IEEE, vol. 101, no. 3, pp. 676-697, 2013.
F. Li, X. Jia, D. Fraser, and A. Lambert, "Super resolution for remote sensing images based on a universal hidden Markov tree model," IEEE Transaction on Geoscience and Remote Sensing, vol. 47, pp. 1270-1278, 2010.
X. Jia and J. A. Richards, “Segmented principal components transformation for efficient hyperspectral remote sensing image display and classification,” IEEE Trans. on Geoscience and Remote Sensing, vol. 37, pp. 538-542, 1999.
J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis, 3rd ed. (1999) and 4th ed. (2006): Springer-Verlag.
Junpeng Zhang; Xiuping Jia; Jiankun Hu; Kun Tan, “Moving Vehicle Detection for Remote Sensing Video Surveillance with Nonstationary Satellite Platform,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 10.1109/TPAMI.2021.3066696, 2021.
Courses associated with Imaging research include:
We also offer a professional education short course for those wishing to upskill.