The CIR Research Area (RA) is one of the long term successful and focused research areas within the Faculty of Business, Justice and Behavioural Sciences. The researchers working in this research area are key members of the Computational Intelligence Research Group within the School of Computing and Mathematics, and their research is well aligned with the major themes of CRiCS (Centre for Research in Complex Systems).
The aim of this RA is to address real world problems in relation to smart information technologies in industries and Australian public sectors with the mission to serve communities through advancing frontier technology. This RA is well aligned with the national research priority Frontier Technologies for Building and Transforming Australian Industries. Under this RA, the group members are to devote their efforts to the four major themes with a wide spectrum of research concerning smart information use covering:
These research themes develop a set of innovative methodologies and techniques for smart information processing and system building for a broad range of practical applications for industries, including mining, engineering, management and health, and public sectors.
The research group accommodates a range of members from earlier career researchers, established researchers and some internationally recognised researchers. Several members have been actively engaged in conducting CRC Projects, ARC Discovery and Linkage Projects, Industry Funded Projects, and Projects funded by international funding bodies such as National Science Foundation of China (NSFC). Most of their research works have been widely published in internationally top ranked journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks, Neural Computation, Machine Learning, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE on Multimedia and major international conference proceedings such as ICIP, ICASSP, ICME, CEC, GECCO and IJCNN.
The group enjoys several dedicated lab facilities established by CRiCS. The labs include a 3G Visualization Lab, a Computer Vision Lab, a Newcrest Mining lab, Robotics lab and High Performance Computing Lab. These labs not only enable the group members to conduct their research activities but also help PhD students to do their research.
|Name||Title/Position||Employer if not Faculty of Business, Justice and Behavioural Sciences, CSU||Location|
|Junbin Gao||Adjunct Professor||Bathurst|
|Michael Antolovich||Senior Lecturer||Bathurst|
|Xiaodi Huang||Senior Lecturer||Albury|
|David Tien||Senior Lecturer||Bathurst|
|Lihong Zheng||Lecturer||Wagga Wagga|
|Chandana Withana||Adjunct Associate Professor||Study Group, Sydney||Sydney|
|Sudath Heiyantuduwage||Adjunct Lecturer||Study Group, Sydney||Sydney|
|Daming Shi||Adjunct Professor||Harbin Institute of Technology and Middlesex University||London, UK Harbin, China|
|Xia Hong||Adjunct Reader||University of Reading||Reading, UK|
|Paul Kwan||Adjunct Senior Lecturer||University of New England||Armidale|
|Zhouchen Lin||Adjunct Professor||Peking University||Beijing, China|
|Project Name||Brief Description||Investigators|
|Privacy of Social Network Site (SNS) users: A Data Mining Solution|
The aim of the study is to explore the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of Social Network Sites (SNS) users AND provide a technical solution to the problem.
|Dr Islam and Dr Yeslam Al-Saggaf|
|Learning on Manifolds||The long term project aims at investigating learning algorithms on manifolds in computer vision.||Prof Junbin Gao|
|Human Gesture and Behaviours Recognition||Human uses gestures to depict sign language to deaf people, convey messages in noisy environments, and interface with computer games. The emphasis of the project is on automatic learning of vocabularies of gestures performed by a single user or several different users under simple or complicated environment. The outcomes of this project can be applied in many interaction and computer vision applications, including computer interaction, video surveillance, rehabilitation and health care.||Dr Lihong Zheng|
|Project Name||Brief Description||Funding Body||Investigators|
|Online Learning for Large Scale Structured Data in Complex Situations||This project is to develop Online Learning algorithms to unlock the potential of such overwhelming data, which can lift existing applications to a new level. Reducing 1% of the total fraud loss of $3.5 trillion by research, is 35 billion dollars return. The advances in fraud detection also make Australia a safer nation which attracts more overseas investments. Advancing social networks by research will secure Australia's future share of the social networks market that has massive potential.||ARC Discovery|
Dr Qinfeng Shi (Uni of Adelaide), Prof Junbin Gao and Prof Sheng Chen (Uni of Southampton, UK)
|A probabilistic framework for nonlinear dimensionality reduction algorithms||The Twin Measures Framework is a novel platform for analysing existing dimensionality reduction methods and the invention of new ones. This research will radically improve image analysis, with beneficial applications from pharmaceutical drug design through to border protection.||ARC Discovery|
Prof Junbin Gao and Prof Xia Hong (Univ of Reading, UK)
|Multi-view video coding using cuboid data compression||This project investigates novel approaches to multiview video coding that use new data compression techniques and explicit occlusion handling. These new approaches complement the state-of-the-art, improving interactivity with instantaneous view change and VCR functionality, reducing encoding complexity and increasing compression efficiency.||ARC Discovery||Prof Manzur Murshed (Monash) and Dr. Manoranjan Paul|
|Drawbell Boulder Detection||This project extends the current research on draw point boulder detection by the Mining Research Laboratory at CSU.||MMT3 Consortium||Prof Junbin Gao, Dr. Michael Antolovich and Mr. Allen Benter|
|Automatic and Natural Clustering of Records||In this study we aim to further improve our clustering techniques in order to group records in more meaningful clusters with automatic cluster number selection, attribute weights for clustering and so on. Clustering results will also be validated by various existing evaluation techniques and novel evaluation techniques.||Faculty of Business, Justice and Behavioural Sciences Compact Fund||Dr Islam, Prof Bossomaier, Prof Estivill-Castro, A/Prof Brankovic|
|Exploiting the Structural Consistency of Multi-modal Data.||The project will study the problem of multi-modal learning, and exploit the inherent complementary visual properties of multi-modal data.||Faculty of Business, Justice and Behavioural Sciences Compact Fund|
Dr Xiaodi Huang and Dr Lin Wu
|Biomedical Image Analysis|
The research project is to develop an automatic, rotation and scale invariant segmentation method to detect contour of horse larynx from endoscopic static image or video for diagnostic as well as surgery procedure under different lighting conditions considering speed and accuracy.
|CSU International PhD Scholarship|
Dr Lihong Zheng and Prof Junbin Gao
|Efficient Low-Resolution Image Segmentation||The similarity measure criteria play critical role in accurate segmentation. This project aims at finding an appropriate similarity measure to extract objects in an image efficiently.||Faculty of Business, Justice and Behavioural Sciences Compact Fund||Dr Lihong Zheng|
This research project is to recognition human gesture based on depth images. Firstly, the discriminatory informative representation for a gesture in 3D space containing disparity gesture information will be identified. Then a 2D-MTM (motion trail model) and a three dimensional motion trail model (3D-MTM) will be built up and applied to demonstrate the accuracy and effectiveness of the proposed 3D-MTM for human gesture recognition.
|CSU International PhD Scholarship||Dr Lihong Zheng|
|Project Name||Brief Description||Funding Body||Investigators|
|Imaging Techniques to Determine Muck Pile Ore Fragment Size In-Situ||We investigate real-time techniques for detection of larger size ore fragment in muck pile under the production environment.||Newcrest Mining Australia||Dr Michael Antolovich and Prof Junbin Gao|
WAREIGS: Wavelet-Network-Based Augmented-Reality-Enhanced Image-Guided Surgery
|This research will address issues with the constraint of surgical openings where surgeons cannot see beyond the exposed surfaces and these limitations are accentuated by the even greater restrictions of minimally invasive surgery. Limited visibility through "keyholes" during endoscopic procedures and through small incisions with ever-diminishing sizes increases the need for intra-operative image guidance. The project aims to develop a system of augmented reality enhanced image guided surgical surgery, in which the images from cameras are aligned with patient's physical position at the time of surgery. Such a "see-through" capability makes surgeries safer and more accurate. two objectives will be achieved: (1) Theoretical research on image registration based on wavelet networks. (2) Development of augmented reality enhanced image-guided surgery with see-through capability.||ACSRF, Australian Government|| Prof Junbin Gao, Dr. Michael Antolovich,|
Prof Terry Bossomaier , Dr. Manoranjan Paul, Dr. Paul Kwan (UNE) and Professor Daming Shi (Univ of Middlesex, UK
|3D Video Coding||The project investigates the video coding technologies for multi-view video coding||Faculty of Business, Justice and Behavioural Sciences Compact Fund||Dr Manoranjan Paul|
|Image Texture Segmentation Based on Wigner Distribution in a Fractional Fourier Domain||To apply the proposed WD-FrFT to image texture segmentation. Since coarseness and directionality are two essential perceptual cues used by the human visual system for discriminating different textures, we will adopt two corresponding types of features, spatial - frequency and orientation, which will be extracted by L1 difference of Gaussian (DOG) filters and L2 wedge filters, respectively.||Faculty of Business, Justice and Behavioural Sciences Compact Fund||Prof Junbin Gao, Dr. Michael Antolovich, Dr. Manoranjan Paul, Dr. Jim Tulip and Dr. David Tien|
|Efficient Low-Resolution Image Segmentation|
The similarity measure criteria play a critical role in accurate segmentation. This project aims at finding an appropriate similarity measure to extract objects in an image efficiently.
|Faculty of Business, Justice and Behavioural Sciences Compact Fund||Dr Lihong Zheng and Prof Junbin Gao|
|Computational Intelligence for Anomaly Detection in Networks: An Investigation||This project investigates the use of computational intelligence (CI) for anomaly detection in computer networks. Also, comparative performances of existing signature-based and misuse detection approaches are investigated.||Faculty of Business, Justice and Behavioural Sciences Compact Fund||Maumita Bhattacharya and Dr Tanveer Zia|
|Age Care Workforce Reform - Building Communities of Practice Around the Prevention of Functional Decline in the Community|
This research seeks to investigate whether improved training and use of technology by clinicians (support workers) and training of volunteers improves human resource management outcomes among employees and volunteer carers who are involved in reducing the rate of functional decline among seniors. This research involves the use of experiments and pre-post surveys of subjects. Various data mining techniques are applied on the survey data for extracting the patterns and information in order to evaluate the impact of various interventions.
|CareWest, Australia||Prof Mark Morrison, Dr Oliver Burmeister, Dr Zahid Islam, Dr Ramudu Bhanugopan and Dr Maree Bernoth|
|Review of Support Worker Integration with Functional Decline||The main aim of this project is to suggest reasons and possible remedies of the functional decline for the employees and care receivers of Hobart District Nursing. We apply data mining and other data analyses on the collected data.||Hobart District Nursing, Australia||Prof Mark Morrisson, Dr Maree Bernoth, Dr Oliver Burmeister, and Dr. Zahid Islam|
|Co-ranking Images and Tags in a Heterogeneous Network|
This project is to develop novel algorithms that are able to handle dual-relational data over a combined graph for co-ranking images and tags simultaneously. In particular, a co-ranking scheme for images and their associated tags in a heterogeneous graph will be designed and tested on real datasets, together with a prototype system.
|Faculty of Business, Justice and Behavioural Sciences Compact Fund|
Dr Xiaodi Huang and Dr Lin Wu
|Data Cleansing and Data Pre-processing Techniques|
In this study we develop novel data cleansing techniques for improving data quality. The improved data will then be used in making better decision for an organisation.
We have also developed an Agent Based Modeling (ABM) which is powered by data mining techniques in order to simulate the property market and thereby predict and assess property prices. We are also developing various techniques for more acceptable property valuation
|Faculty of Business, Justice and Behavioural Sciences;Compact Fund||Dr Islam, Prof Terrry Bossomaier, Prof Junbin Gao|
|Data Mining threats on Privacy of Social Network Site (SNS) users||The aim of the study is to explore the potential of data mining as a technique that could be used by malicious data miners to threaten the privacy of Social Network Sites (SNS) users.||Faculty of Business, Justice and Behavioural Sciences Compact Fund||Dr Yeslam Al-Saggaf and Dr Islam|
|Learning based Boundary/Contour Extraction||Automatic Number Plate Recognition (ANPR) is required due to increasing traffic management. This project will deliver a technology capable of efficiently extracting trustful boundary feature for car number plates.||CSU Small Grant||Dr Lihong Zheng|
|Efficient Character Segmentation in ANPR||This project will deliver an accurate and efficient technology to cut characters from located images of car number plates.||Faculty Seed Grant||Dr Lihong Zheng|
|A Novel Decision Tree Classification Algorithm||The aim of this project is to develop a novel decision tree algorithm that will extract useful patterns (that are currently ignored by existing algorithms) from a data set. We study various existing classification algorithms such as Decision tree algorithms, Neural networks, Bayesian algorithms and Genetic algorithms. We propose some modifications to existing algorithms and test the algorithm by applying it on a number of data sets. We compare the efficiencies of the proposed algorithm with various existing algorithms such as See 5, J48, and REPTree. The efficiencies are evaluated based on quality of extracted pattern, simplicity of the trees, Performance and Significance of logic rules. Our initial experimental results are very encouraging.||CSU Small Grant 2009||Dr Islam|
|Timothy Dobson||Hons||Representation learning in games technology.|
Dr Jim Tulip
|Saliya Wimalartne||Hons||Investigation on a biologically inspired heuristic and its application to computer security.|| Maumita Bhattacharya|
02 6051 9619
The following opportunities exist in Computational Intelligence and Robotics:
| Dr Manoranjan Paul|
02 6338 4260
| Dr Zahid Islam|
02 6933 2415
| Dr Lihong Zheng|
02 6933 2387
| Maumita Bhattacharya|
02 6051 9619
| Dr Xiaodi Huang|
02 6051 9652
Dr Manoranjan Paul
Dr Zahid Islam
Dr Lihong Zheng
Dr Xiaodi Huang