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Invariant Human Pose Feature Extraction for Movement Recognition and Pose Estimation

Abstract Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose feat... (more)
Created Date 2011
Contributor Peng, Bo (Author) / Qian, Gang (Advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Subject Computer Engineering / Electrical Engineering / Computer Science
Type Doctoral Dissertation
Extent 126 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Electrical Engineering 2011
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis