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A Statistical Approach to Structure and Motion from Image Features

Åström, Karl LU ; Kahl, Fredrik LU ; Heyden, Anders LU and Berthilsson, Rikard LU (1998) Advances in Pattern Recognition. Joint IAPR International Workshops. SSPR'98 and SPR'98. Proceedings In [Host publication title missing] p.929-936
Abstract
The estimation of structure and motion from image sequences using corresponding points, lines, conics and structured patches is treated. Recent research has provided good tools for obtaining good initial estimates of structure and motion using point, line, conic and curve correspondences. These estimates are, however, not so accurate. It is shown how to obtain statistically optimal estimates of structure and motion using a combination of such image feature correspondences. The question of using proper weighting is important when different types of features are combined. We show how weights can be chosen in a statistical optimal sense. Experiments with real data are used to evaluate every step of the algorithm
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
computational geometry, image motion analysis, image sequences. statistical analysis
in
[Host publication title missing]
pages
929 - 936
publisher
Springer
conference name
Advances in Pattern Recognition. Joint IAPR International Workshops. SSPR'98 and SPR'98. Proceedings
external identifiers
  • Scopus:84947745451
ISBN
3 540 64858 5
language
English
LU publication?
yes
id
9dbabe0e-85ed-42e4-bedd-a4b9e875b3e9 (old id 787603)
date added to LUP
2008-03-31 13:59:56
date last changed
2016-10-13 04:42:06
@misc{9dbabe0e-85ed-42e4-bedd-a4b9e875b3e9,
  abstract     = {The estimation of structure and motion from image sequences using corresponding points, lines, conics and structured patches is treated. Recent research has provided good tools for obtaining good initial estimates of structure and motion using point, line, conic and curve correspondences. These estimates are, however, not so accurate. It is shown how to obtain statistically optimal estimates of structure and motion using a combination of such image feature correspondences. The question of using proper weighting is important when different types of features are combined. We show how weights can be chosen in a statistical optimal sense. Experiments with real data are used to evaluate every step of the algorithm},
  author       = {Åström, Karl and Kahl, Fredrik and Heyden, Anders and Berthilsson, Rikard},
  isbn         = {3 540 64858 5},
  keyword      = {computational geometry,image motion analysis,image sequences. statistical analysis},
  language     = {eng},
  pages        = {929--936},
  publisher    = {ARRAY(0xaae5bb8)},
  series       = {[Host publication title missing]},
  title        = {A Statistical Approach to Structure and Motion from Image Features},
  year         = {1998},
}