Mr Kiran Varanasi / Dr Fabio Cuzzolin Applicant Career Summary

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Mr Kiran Varanasi / Dr Fabio Cuzzolin Newton International Fellowships - 2010 Statement of qualifications and career: Field of Specialisation: Publications: Subject: Present Research: Present Position: Present Employer: Present Department: Marie Curie Fellow, INRIA Rhone-Alpes, Grenoble, France Post-doctoral researcher, University of California at Los Angeles Fixed-term assistant professor, Politecnico di Milano, Italy Lecturer - Early Career Fellow Oxford Brookes University Department of Computing Created: Friday, January 15, 2010 15:09 [Approved] Qualification Date Lecturer, Oxford Brookes University 01/09/2008 - to date 04/09/2006 - 03/09/2008 01/10/2004-09/04/2006 01/01/2003-31/12/2004 Post-doctoral researcher, University of Padua, Italy 01/06/2001-31/05/2003 Dr Cuzzolin’s research interests include machine and manifold learning, computer vision and human motion analysis, the theory of belief functions and imprecise probabilities. Fabio Cuzzolin, Multilinear modeling for robust identity recognition from gait, in “Behavioral Biometrics for Human Identification: Intelligent Applications", Liang Wang and Xin Geng (Eds.), IGI Publishing, 2009 Fabio Cuzzolin, A geometric approach to the theory of evidence, IEEE Transactions on Systems, Man, and Cybernetics part C, 38(4), pages 522-534, July 2008 Fabio Cuzzolin, Diana Mateus, David Knossow, Edmond Boyer, and Radu Horaud, Coherent Laplacian protrusion segmentation, Proceedings of CVPR'08, Anchorage, Alaska; Diana Mateus, Radu Horaud, David Knossow, Fabio Cuzzolin, and Edmond Boyer, Articulated Shape Matching Using Laplacian Eigenfunctions and Unsupervised Point Registration, Proceedings of CVPR'08, Anchorage, Alaska; Fabio Cuzzolin, Using Bilinear Models for View-invariant Action and Identity Recognition, Proceedings of CVPR'06, pp. 1701-1708, New York, June 18-22 2006 NIF Group 05: Information communication technology (ICT) / Computer Vision - ICT Dr Cuzzolin’s research interests include machine learning, computer vision and imprecise probabilities. He is first or single author of some 50 publications (including 9 journals + 6 under review), some of which received awards. He collaborates with several journals in both computer vision and probabilities, and served in the program committee of some 15 international conferences. Dr Cuzzolin is a prominent expert in the field of random sets. He formulated a geometric approach to uncertainty in which probabilities, possibilities and belief functions can all be represented as points of a Cartesian space and there analyzed. He studied how to approximate random sets with probabilities, and proposed novel formulations of the theory of belief functions. Within computer vision, his work focused on human motion analysis and action recognition. He proposed the use of multilinear models for identity recognition from gait, and explored spectral motion capture techniques for unsupervised 3D segmentation and matching. Dr Cuzzolin is finalizing collaborations with IDSIA, Switzerland for a STREP on imprecise Markov chains for gesture recognition, and with INRIA, Pompeu Fabra and Technion on a Future and Emerging Technology (FET) EU proposal on large scale manifold learning. He is discussing a collaborative project on uncertainty theory at UK level with U. Bristol and Durham’s Dept of Statistics. He is also exploring the opportunity of a European Network of Excellence in the same fiel Page 4 of 8
Mr Kiran Varanasi / Dr Fabio Cuzzolin Newton International Fellowships - 2010 Present Position Start Date: Present Position End Date: PhD Awarded Date: Proposal Current Funding Description: Previous Support Description: Subject: Project Title: Start Date: End Date: Research proposal: 01/09/2008 31/08/2013 19/02/2001 Created: Friday, January 15, 2010 15:09 [Approved] Dr Cuzzolin is currently applying for his EPSRC First Grant, he is finalizing a FET (Future Emerging Technology) EU FP7 project with as partners INRIA Rhone- Alpes, U. Pompeu Fabra (Barcelona), and Technion (Haifa). He is also preparing a Leverhulme research proposal, and will submit a European Research Council Starting Grant before October 2010. No funding though is currently available. Dr Cuzzolin has received Oxford Brookes' central university funding in 2008 (some 3600 pounds overall). NIF Group 05: Information communication technology (ICT) / Computer Vision - ICT Manifold learning for motion analysis and recognition 01/01/2011 31/12/2012 In the course of this project, we will explore the problem of recognizing objects and their motion in an unknown environment filled with background clutter. One good example is the recognition of human activities in an outdoor environment, from a single image or from a set of images, obtained from cameras or depth sensors. In this case, we aim to recognize the human beings in the scene, their body postures and their motion over time. Our problem is made complex by the facts that the people need not be visible entirely to the camera, that they might wear loose clothes which cause further occlusions, and that they might interact with each other using various unknown objects. In contrast, if it is known that a person faces straight to the sensor, or at least, that he/she is visible entirely to the sensor's field of view, a known articulated model of human beings can be fit to the observed image and the pose of the body be computed. Certain commercial applications are in the course of development (e.g, Project Natal of Microsoft corporation) which use cameras and depth sensors in this way, to compute body poses and provide novel modes of interaction for users to play computer games. The variations in human body shapes and their poses are learnt into a statistical model, using a vast database of actors captured in a pre-defined set of poses. Apart from commercial ventures, there have also been various research publications that studied this problem in the recent past. Diverse solutions have been proposed to define and represent such statistical model, and to compute the closest fit for an observation. One such solution was proposed by Dr. Cuzzolin et al, through manifold learning. The observation is transformed into a new space using adaptive spectral embeddings that are isometrically invariant (i.e, that are invariant to articulated motions). Articulated motion can be estimated as a simple global rotation in this embedding space. The power of this solution is its generality, as it can be used for motion estimation of an unknown object, not necessarily a human being. However, its weakness is that the object needs to be segmented properly from the background, which is difficult to achieve in a natural setting, or when multiple actors are present. Page 5 of 8
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