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Keynote I: High dimensional data analysis in Computer Vision

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1 Author(s)
Suter, D. ; Dept. of Electr. & Comput. Syst. Eng., Monash Univ., Clayton, VIC

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