Shape and pose parameter estimation of 3D multi-part objects

Satoshi Yonemoto, Naoyuki Tsuruta, Rin Ichiro Taniguchi

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

1 Citation (Scopus)

Abstract

This paper presents an analysis-by-image-synthesis framework of shape and pose estimation of 3D multi-part objects, whose purpose is to map objects in the real world into virtual environments. In general, complex 3D multi-part objects cause serious self-occlusion and non-rigid motion. To deal with the occlusion among them, we employ both multiple calibrated cameras and time-varying sequences, since there is enough information to estimate the parameters in the sensory data. In our framework, to minimize the error between the selected measurements and the estimated model parameters, we proceed model fitting process based on proper gradient-based minimization.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 1998 - 3rd Asian Conference on Computer Vision, Proceedings
EditorsRoland Chin, Ting-Chuen Pong
PublisherSpringer Verlag
Pages479-486
Number of pages8
ISBN (Print)3540639314, 9783540639312
Publication statusPublished - Jan 1 1997
Event3rd Asian Conference on Computer Vision, ACCV 1998 - Hong Kong, Hong Kong
Duration: Jan 8 1998Jan 10 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1352
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd Asian Conference on Computer Vision, ACCV 1998
CountryHong Kong
CityHong Kong
Period1/8/981/10/98

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yonemoto, S., Tsuruta, N., & Taniguchi, R. I. (1997). Shape and pose parameter estimation of 3D multi-part objects. In R. Chin, & T-C. Pong (Eds.), Computer Vision - ACCV 1998 - 3rd Asian Conference on Computer Vision, Proceedings (pp. 479-486). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1352). Springer Verlag.