Feature preserving motion compression based on hierarchical curve simplification

The authors have been studying motion database systems. When entering an example motion as the query for the similarity search of motion data, it is natural to enter it as a semantic primitive motion, i.e., "walk", "jump", "run" and so on. Mostly, one motion data consis...

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Bibliographic Details
Main Authors: Etou, H., Okada, Y., Niijima, K.
Format: Conference Proceeding
Language:English
Subjects:
Applied sciences
Artificial intelligence
Character generation
Computer science
control theory
systems
Control systems
Data mining
Database systems
Deductive databases
Exact sciences and technology
Humans
Intelligent control
Motion control
Motion pictures
Pattern recognition. Digital image processing. Computational geometry
Production
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Summary:The authors have been studying motion database systems. When entering an example motion as the query for the similarity search of motion data, it is natural to enter it as a semantic primitive motion, i.e., "walk", "jump", "run" and so on. Mostly, one motion data consists of several primitive motions. It is necessary to divide a composite motion into primitive motions. There are no algorithms able to automatically divide a composite motion into semantic primitive motions perfectly because the semantic meanings of primitive motions are strongly depending upon the human senses. A curve simplification algorithm is used for the key-posture extraction from motion data. This helps us to divide a composite motion into its primitive motions. The key-posture extraction is also used for the motion compression. In this paper, the authors propose a new efficient key-posture extraction method that hierarchically applies the curve simplification algorithm to the feature joints of a human figure model
DOI:10.1109/ICME.2004.1394504