| Publications » |
| Students » |
| Projects » |
Prospective Students
I am always in the look out for motivated and capable research students. Feel free to e-mail me (see the "Contact me" item on the left frame). For more information, please go to the "PhD Opportunities" item on the Students menu on the left. Note that, if you are a first class student, which is motivated to pursue a PhD, NICTA has PhD scholarships available on a competitive basis (visit the education site for details)
Research Interests
My research interests are in the areas of Pattern Recognition, Computer Vision, Spectral Imaging and Computer Graphics. Along these lines, I have done work on segmentation and grouping, tracking, graph-matching, shape analysis, the understanding of images beyond the visible spectrum and reflectance models. I am also interested in more theoretical topics such as the differential structure of discrete surfaces and graphical models for recognition and classification.My work has found application in the recovery of the 3D structure of an object from single and multiple views, the recovery of the BRDF from single images, content-based image database indexing and retrieval and, more recently, spectral image understanding and recognition.
At present, I am working on computer vision and pattern recognition applied to the areas of environmental management, biosecurity, biometrics and surveillance systems.
Brief Vitae
I received my B.Eng. degree in Electronics and
Telecommunications from the Inst.
Tecnologico y de Estudios Superiores de Monterrey (ITESM) with honours in
1998. In 1999, after a year in industry, I enrolled in a PhD programme at the University of York. In
2001, being a graduate student, I was granted the Gibbs/Plessey Award
to the best research proposal to visit an overseas research lab. This is a competitive award which is
granted in consultation with GEC Marconi.
I completed my PhD in Computer Science in 2003.
After receiving my doctorate, I remained in York until Dec. 2004 as a Research Associate
under the MathFit-EPSRC framework. My main task was to develop algorithms and do basic research
on problems which may be posed in a continuous and combinatorial setting making use of differential geometry.
This was with a twofold aim. First, developing a mathematical and computational framework for the
recovery of surface height data from fields of surface normals. Secondly,
the development of efficient relational matching and database indexing algorithms for
purposes of context-based image retrieval.
In 2005, I moved to Australia and took a research scientist appointment with National ICT Australia (NICTA)
at the Canberra Lab. Together with this appointment, I became an Adjunct Research Fellow at the ANU. After working on surveillance systems with query capabilities, in 2006 I was appointed the
project leader of the "Spectral Imaging and Source Mapping (SISM)
project and promoted to Senior Researcher. Also in 2006, Roland Goecke and myself
started the VisHCI workshop, which
ran in 2006 and 2007 mainly funded by the HCSNet.
From 2007 to 2009, I was a Postdoctoral Research Fellow of the Australian Research Council.
These are highly competitive fellowships. Mine was awarded for the project entitled "Spectral Mutli-camera Tracking" (see the
"Multicamera tracking" item on the left menu for more details). I was the general chair
of DICTA 2008 and area chair of DICTA 2009 and ACCV 2010. In 2009 I also
became a Conjoint Senior Lecturer at the School of Inf. Tech. and Electrical Engineering of the
University of New South Wales at the Australian Defence Force Academy (UNW@ADFA). In 2010 I became the manager of the
International Science Linkages (ISL) project entitled "Graph-based Representations of Remotely
Sensed Data for Geoindexing Applications". The project, with Zhejiang University, corresponds to the Round 9 of the
Australia-China Special Fund for S&T Cooperation, which is jointly managed
by the Australian Government Department of Innovation, Industry, Science and Research (DIISR) and
its Chinese counterpart, the Ministry of Science and Technology (MOST).
