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Keynote I: High dimensional data analysis in Computer Vision

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1 Author(s)
David Suter ; Department of Electrical and Computer Systems Engineering, Monash University, Australia

Summary form only given. Computer vision (the study of extracting information from images that includes robot vision, smart video surveillance, multi-media image search, camera-based human computer interfaces, etc.) deals with very large data rates: but it generally also has to contend with high-dimensional data and incomplete data and noise. The basic tools underpinning much of contemporary computer vision research: clustering, large (and possibly incomplete) matrix factorization, regression/model fitting, manifold learning etc.; are tools common to many other branches of computing. In this article, the author draw upon examples from his own research work to outline recent advances in dealing with high-dimensional data. Illustrative applications is given from computer vision problems (with some links made to other application areas).

Published in:

Computer and Information Technology, 2008. CIT 2008. 8th IEEE International Conference on

Date of Conference:

8-11 July 2008