Abstract
This tutorial provides a detailed study of graph-based methods in pattern recognition. The course aims at covering the fundamental principles of stochastic, spectral, probabilistic and manifold based methods related with graphs and their applications to segmentation and grouping, matching, classification and recognition. The tutorial will also cover recent trends and developments related to deep networks and link inference in the structural pattern analysis space.
Short Bio
Antonio Robles-Kelly received a B.Eng. degree in Electronics and Telecommunications with honours in 1998 and a PhD in Computer Science from the University of York, UK, in 2003. He remained in York under the MathFit-EPSRC framework and, in 2005, he moved to Australia and took a research scientist appointment with National ICT Australia (NICTA). In 2006 he became the project leader at NICTA and, from 2007 to 2009, he was a Postdoctoral Research Fellow of the Australian Research Council. In 2016, he joined CSIRO where he is a Principal Researcher with Data61 and, in 2018 he moved to Deakin University, where he is a Professor of Machine Learning and the Associate Head of School of IT (Research). Dr Robles-Kelly’s research has been applied to areas such as logistics, infrastructure planning, biosecurity, forensics, food quality assurance and biometrics and is now being commercialised under the trademark of Scyllarus (www.scyllarus.com). He has served as the president of the Australian Pattern Recognition Society (APRS) and is an associate editor of the Pattern Recognition Journal and the IET Computer Vision Journal. He is a Senior Member of the IEEE, the president of the TC2 (Technical Committee on structural and syntactical pattern recognition) of the International Association for Pattern Recognition (IAPR), an Adjunct Academic at the ANU and a Visiting Scientist at CSIRO Astronomy and Space. He has also been a technical committee member, area and general chair of several mainstream machine learning, computer vision and pattern recognition conferences.
Francisco Escolano received his Bachelors degree in Computer Science from the Polytechnical University of Valencia (Spain) in 1992 and his PhD degree in Computer Science from the University of Alicante in 1997. Since 1998, he has been an Associate Professor with the Department of Computer Science and Artificial Intelligence of the University of Alicante. He has been post-doctoral fellow with Dr. Norberto M. Grzywacz at the Biomedical Engineering Department of the University of South California in Los Angeles, and he has also collaborated with Dr. Alan L. Yuille at the Smith-Kettlewell Eye Research Institute of San Francisco. Recently, he visited the Liisa Holm’s Bioinformatics Lab at the University of Helsinki. His research interests are focused on the development of efficient and reliable computer vision algorithms for biomedical applications (tracking of intravascular sequences), active vision and robotics (mid-level geometric structures obtained through junction grouping, stereo and appearance based methods for the localization of mobile robots, SLAM), and video-based surveillance (motion detection and object tracking). He is also interested in the coupling between computer and biological vision. He is the head of the Robot Vision Group and the Vice-president of the TC2 (Technical Committee on structural and syntactical pattern recognition) of the International Association for Pattern Recognition (IAPR). He has been a technical committee member, area and general chair of several mainstream computer vision and pattern recognition conferences.