Feature preserving motion compression based on hierarchical curve simplification

Hiroaki Etou, Yoshihiro Okada, Koichi Niijima

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

The authors have been studying on motion database systems. When entering an example motion as the query for the similarity search of motion data, it is natural way 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 sense. 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 new efficient key-posture extraction method that hierarchically applies the curve simplification algorithm to the feature joints of a human figure model.

Original languageEnglish
Title of host publication2004 IEEE International Conference on Multimedia and Expo (ICME)
Pages1435-1438
Number of pages4
Publication statusPublished - 2004
Event2004 IEEE International Conference on Multimedia and Expo (ICME) - Taipei, Taiwan, Province of China
Duration: Jun 27 2004Jun 30 2004

Publication series

Name2004 IEEE International Conference on Multimedia and Expo (ICME)
Volume2

Other

Other2004 IEEE International Conference on Multimedia and Expo (ICME)
CountryTaiwan, Province of China
CityTaipei
Period6/27/046/30/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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