Globally -Coordinated Locally-Linear Modeling of Multi-Dimensional Data

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Liu, Che-Bin

Contributors dc:contributor

• Ahuja, Narendra

• Computer Science

Language dc:language

eng

Identifier

(MiAaPQ)AAI3223659

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/81723