2026-09-21T00:26:58Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81723 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

http://hdl.handle.net/2142/81723 Ahuja, Narendra Liu, Che-Bin 2015-09-25T20:20:10Z 2015-09-25T20:20:10Z 10000-01-01 2006 2006 The proposed approach in this thesis has advantages over existing approaches to analyzing complex spatio-temporal data. Experiments show that the new modeling features of our approach improve the performance of existing approaches in many applications. In object tracking, our approach is the first one to track nonlinear appearance variations by using low-dimensional representation of the appearance change in globally-coordinated linear subspaces. In dynamic texture synthesis, we are able to model non-stationary dynamic textures, which cannot be handled by any of the existing approaches. In human motion synthesis, we show that realistic synthesis can be performed without using specific transition points, or key frames. Made available in DSpace on 2015-09-25T20:20:10Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3223659.pdf: 3144462 bytes, checksum: e2fb3e5e943ab021fa8fc1a62070c4b2 (MD5) Previous issue date: 2006 Embargo set by: Seth Robbins for item 83004 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 115 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. (MiAaPQ)AAI3223659 eng Computer Science Globally -Coordinated Locally-Linear Modeling of Multi-Dimensional Data text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.