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Past Project Staff

erik_b
Dr Erik Berglund
Role  
UQ Research Fellow, Research Engineer, SAFE Sensors, NICTA
Interests
Chaotic dynamics in recursive networks, face recognition, and implicit data processing
Affiliations
ITEE, The University of Queensland, NICTA
Biography
Dr. Erik Berglund received the equivalent of a Bachelor's degree in Computer Engineering from Ostfold University College in 2000 and a Ph.D from the University of Queensland in 2006. He is currently a senior research fellow at UQ.




ting_s
Dr Ting Shan
Role  
UQ Research Fellow, Researcher, SAFE Sensors, NICTA
Interests
Computer Vision, Pattern Recognition and Content-Based Image Retrieval
Affiliations
ITEE, The University of Queensland, NICTA
Biography
Researcher with the NSST project, of SAFE Sensors work package in the Smart Applications For Emergencies (SAFE) program at NICTA's Queensland Research Laboratory. Developed two face recognition approaches, particularly for face images under different pose angles, and a real-time face recognition system during his Ph.D.
 ian_cullinan
Ian Cullinan

Role 
Research Engineer, Advanced Surveillance Project, NICTA
Interests
Embedded systems, digital design, performance optimisations
Affiliations
ITEE, University of Queensland, NICTA
Biography
Ian is working part-time on the Advanced Surveillance project at NICTA while he completes his BE (Computer Systems) at UQ. He also tutors for UQ.




 

Past Visiting Researchers


Prof Hans Burkhardt
Role  
International Distinguished Visitor
Interests
Computer Vision, Pattern Recognition
Affiliations
Albert-Ludwigs-University, Freiburg Germany
Biography
Hans Burkhardt obtained his Dipl.-Ing. degree in electrical engineering in 1969, Dr.-Ing. degree in 1974, and the Venia Legendi in 1979 from the University of Karlsruhe, Germany. From 1969 he was Research Assistant and in 1975 he became Lecturer at the University of Karlsruhe. During 1980-81 he had a scientific fellowship at the IBM Research Laboratory, San Jose, CA. In 1981 he became Professor for Control and Signal Theory at the University of Karlsruhe. During 1985-1996 he was full Professor at the Technical University of Hamburg and director of an Institute in the Computer Science Department and additionally scientific advisor between 1990 and 1996 for the Microelectronic Application Center (MAZ) in Hamburg. Since 1997 he is full Professor at the Computer Science Department of the University of Freiburg; director of an Institute for Pattern Recognition and Image Processing and currently Deputy Dean of the Faculty for Applied Sciences. Since 2000 he is president of the German Association for Pattern Recognition (DAGM). He is a member of the "Academy of Sciences and Humanities, Heidelberg", of  “acatech” (Council of Technical Sciences of the German Academies of Sciences) and a Fellow of the International Association for Pattern Recognition (IAPR). 2003/2004 he was on a sabbatical leave for half a year as a Visiting Researcher at the National ICT (NICTA) at the Australian National University (ANU) in Canberra, Australia.

He has published over 200 papers and given more than 250 lectures. He is a consultant for several national and international institutions e.g. the German Science Foundation (DFG), the European Commission and different international organizations and journals. In 1998 he was chair of the European Conference on Computer Vision (ECCV).

Experience: Invariants in pattern recognition, optimal image restoration methods, motion estimation algorithms, parallel algorithms in image processing and pattern recognition, image analysis and vision guided control of combustion processes.

Contact Information:
Prof. Dr. Hans Burkhardt
Computer Science Department, Albert-Ludwigs-University, Freiburg Germany
Georges-Koehler-Allee 052, 79110 Freiburg i.Br., Germany
phone: +49-761-203-8260, fax: +49-761-203-8262
email: Hans.Burkhardt@informatik.uni-freiburg.de
URL: http://lmb.informatik.uni-freiburg.de/



Dr Manas Bhuyan
Role  
International Visitor
Interests
  Image/Video Processing, Computer Vision and Human Computer Interactions (HCI)
Affiliations
IIT Guwahati, India
Biography
Dr. Manas Kamal Bhuyan received B.E (Hons) in Electrical Engineering from Dibrugarh University, Assam. He received M.E. (Hons) degree in Instrumentation Engineering from Jadavpur University, Calcutta and Ph.D degree in Electronics and Communication Engineering from Indian institute of Technology Guwahati, India. He is now working as a visiting fellow/researcher in the school of ITEE of University of Queensland, Australia and National ICT, Brisbane, Australia under BOYSCAST fellowship offered by the Department of Science and Technology, Government of India. His broad research interests include Image/Video Processing, Computer Vision and Human Computer Interactions (HCI).


Past Students

suyu_k
Suyu Kong
Role  
Former Research Student
Interests
Computer Vision
Affiliations
ITEE, The University of Queensland
Biography
Suyu Kong is a former MPhil student in the school of ITEE, The University of Queensland and National ICT, Australia who has recently graduated. His research interests include multiple person tracking and classification, and traffic surveillance.


felix_warner
Felix Werner
Role  
Research Student
Interests
My research is in vision-based topological localisation for autonomous mobile robots. Further interests include mobile robotics, computer vision, neural networks, biologically inspired robots, machine learning and face detection.
Affiliations
FIT, Queensland University of Technology, NICTA
Biography
10/1999 - 10/2005: Study of Computer Science at the University of Tübingen, Germany. 2/2005 - 8/2005: Diploma Thesis at Robowatch Technologies, Berlin, Germany 10/2005 - 2/2006: research assistant at the Department of Computer Architecture, Eberhard-Karls-University Tübingen since 4/2006 PhD student at Queensland University of Technology, Australia and the NICTA Queensland Lab.


amelia_azman Amelia Wong Azman

Role  
Research Student
Interests
Embedded Systems, Computer Vision
Affiliations
ITEE, The University of Queensland; ECE, International Islamic University Malaysia
Biography
Amelia received a Bachelor of Engineering degree in Electronics from the University of Southampton in 2004.

Research
Amelia's research is a part of a bigger research project on smart camera systems. Recently, there has been a widespread stress on IP-reuse on SoCs in order to bridge the gap between the silicon capacity in SoC and the design productivity. This research work introduces a framework that combines reasoning of Bayesian Belief Network (BBN) to speed up decision time in partitioning IP cores with CSP formulations to acknowledge the interface costs of its manifestation as well as providing scheduling information in the system design. The framework also support multi-processor system designs. BBN is a form of probabilistic graphical modeling which uses inference and reasoning to produce a possible solution. BBN has several advantages over Neural Network such as the explainable learning process and is more flexible design topology while avoiding data-overfitting. In this work, the BBN is utilised to predict a suitable implementation for a particular process i.e. whether the process is to be implemented as dedicated hardware or software in a microprocessor.
yasir_mustafa Yasir Mustafah

Role  
Research Student
Interests
Computer Vision, Image Processing, Embedded Systems
Affiliations
ITEE, The University of Queensland; ECE, International Islamic University Malaysia
Biography
Yasir received the BE degree from University of Southampton in 2004.

Research
Smart cameras can improve the existing surveillance systems by making autonomous video surveillance possible. Having a smart camera with a good face detection capability is the key in building a face recognition system for crowd monitoring activity. Since crowd monitoring requires high resolution video images, the face detection on the smart camera must be robust and fast enough to achieve real-time operation. The research aims to explore a possible implementation of robust face detection on smart camera hardware that is suitable for real-time surveillance operation. The general approach isperforming the detection in two stages. In the first stage, fast pixel-based algorithms that can be implemented next to the image sensor are utilize to roughly estimate the location of the faces. Then a more complex face detection algorithm is used to accurately locate the estimated faces.