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Low complexity differential geometric computations with applications to human activity analysis

Abstract In this thesis, we consider the problem of fast and efficient indexing techniques for time sequences which evolve on manifold-valued spaces. Using manifolds is a convenient way to work with complex features that often do not live in Euclidean spaces. However, computing standard notions of geodesic distance, mean etc. can get very involved due to the underlying non-linearity associated with the space. As a result a complex task such as manifold sequence matching would require very large number of computations making it hard to use in practice. We believe that one can device smart approximation algorithms for several classes of such problems which take into account the geometry of the manifold and maintain the favorable properties of... (more)
Created Date 2012
Contributor Anirudh, Rushil (Author) / Turaga, Pavan (Advisor) / Spanias, Andreas (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / Computer science / activity recognition / Differential geometry / low complexity analysis / manifold sequences / symbolic approximation
Type Masters Thesis
Extent 42 pages
Language English
Reuse Permissions All Rights Reserved
Note M.S. Electrical Engineering 2012
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis