Euclidean structure from uncalibrated images
(1994) BMVC94. Proceedings of the 5th British Machine Vision Conference 2. p.509-518- Abstract
- A number of recent papers have demonstrated that camera “self-calibration” can be accomplished purely from image measurements, without requiring special calibration objects or known camera motion. We describe a method, based on self-calibration, for obtaining (scaled) Euclidean structure from multiple uncalibrated perspective images using only point matches between views. The method is in two stages. First, using an uncalibrated camera, structure is recovered up to an affine ambiguity from two views. Second, from one or more further views of this affine structure the camera intrinsic parameters are determined, and the structure ambiguity reduced to scaled Euclidean. The technique is independent of how the affine structure is obtained. We... (More)
- A number of recent papers have demonstrated that camera “self-calibration” can be accomplished purely from image measurements, without requiring special calibration objects or known camera motion. We describe a method, based on self-calibration, for obtaining (scaled) Euclidean structure from multiple uncalibrated perspective images using only point matches between views. The method is in two stages. First, using an uncalibrated camera, structure is recovered up to an affine ambiguity from two views. Second, from one or more further views of this affine structure the camera intrinsic parameters are determined, and the structure ambiguity reduced to scaled Euclidean. The technique is independent of how the affine structure is obtained. We analyse its limitations and degeneracies. Results are given for images of real scenes. An application is described for active vision, where a Euclidean reconstruction is obtained during normal operation with an initially uncalibrated camera. Finally, it is demonstrated that Euclidean reconstruction can be obtained from a single perspective image of a repeated structure (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/787182
- author
- Armstrong, M ; Zisserman, A and Beardsley, P
- publishing date
- 1994
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- active vision, image reconstruction, uncalibrated images, self-calibration, image measurements, Euclidean structure, perspective images, Euclidean reconstruction, camera intrinsic parameters
- host publication
- BMVC94. Proceedings of the 5th British Machine Vision Conference
- volume
- 2
- pages
- 509 - 518
- publisher
- BMVA Press
- conference name
- BMVC94. Proceedings of the 5th British Machine Vision Conference
- conference location
- Guildford, United Kingdom
- conference dates
- 1994-09-13 - 1994-09-16
- ISBN
- 0 9521898 1 X
- language
- English
- LU publication?
- no
- id
- 14445a24-daef-4a97-9057-4e64fe6068b5 (old id 787182)
- date added to LUP
- 2016-04-04 12:05:53
- date last changed
- 2018-11-21 21:08:59
@inproceedings{14445a24-daef-4a97-9057-4e64fe6068b5, abstract = {{A number of recent papers have demonstrated that camera “self-calibration” can be accomplished purely from image measurements, without requiring special calibration objects or known camera motion. We describe a method, based on self-calibration, for obtaining (scaled) Euclidean structure from multiple uncalibrated perspective images using only point matches between views. The method is in two stages. First, using an uncalibrated camera, structure is recovered up to an affine ambiguity from two views. Second, from one or more further views of this affine structure the camera intrinsic parameters are determined, and the structure ambiguity reduced to scaled Euclidean. The technique is independent of how the affine structure is obtained. We analyse its limitations and degeneracies. Results are given for images of real scenes. An application is described for active vision, where a Euclidean reconstruction is obtained during normal operation with an initially uncalibrated camera. Finally, it is demonstrated that Euclidean reconstruction can be obtained from a single perspective image of a repeated structure}}, author = {{Armstrong, M and Zisserman, A and Beardsley, P}}, booktitle = {{BMVC94. Proceedings of the 5th British Machine Vision Conference}}, isbn = {{0 9521898 1 X}}, keywords = {{active vision; image reconstruction; uncalibrated images; self-calibration; image measurements; Euclidean structure; perspective images; Euclidean reconstruction; camera intrinsic parameters}}, language = {{eng}}, pages = {{509--518}}, publisher = {{BMVA Press}}, title = {{Euclidean structure from uncalibrated images}}, volume = {{2}}, year = {{1994}}, }