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https://hdl.handle.net/2440/116359
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Type: | Conference paper |
Title: | Robust techniques for the estimation of structure from motion in the uncalibrated case |
Author: | Brooks, M.J. Chojnacki, W. van den Hengel, A. Baumela, L. |
Citation: | Lecture Notes in Artificial Intelligence, 1998 / Burkhardt, H., Neumann, B. (ed./s), vol.1406, pp.281-295 |
Publisher: | Springer |
Issue Date: | 1998 |
Series/Report no.: | Lecture Notes in Computer Science; 1406 |
ISBN: | 3540645691 9783540645696 |
ISSN: | 0302-9743 1611-3349 |
Conference Name: | 5th European Conference on Computer Vision (ECCV'98) (2 Jun 1998 - 6 Jun 1998 : Freiburg, Germany) |
Editor: | Burkhardt, H. Neumann, B. |
Statement of Responsibility: | Michael J. Brooks, Wojciech Chojnacki, Anton van den Hengel, Luis Baumela |
Abstract: | Robust techniques are developed for determining structure from motion in the uncalibrated case. The structure recovery is based on previous work [7] in which it was shown that a camera undergoing unknown motion and having an unknown, and possibly varying, focal length can be self-calibrated via closed-form expressions in the entries of two matrices derivable from an instantaneous optical flow field. Critical to the recovery process is the obtaining of accurate numerical estimates, up to a scalar factor, of these matrices in the presence of noisy optical flow data. We present techniques for the determination of these matrices via least-squares methods, and also a way of enforcing a dependency constraint that is imposed on these matrices. A method for eliminating outlying flow vectors is also given. Results of experiments with real-image sequences are presented that suggest that the approach holds promise. |
Rights: | © Springer-Verlag Berlin Heidelberg 1998 |
DOI: | 10.1007/BFb0055673 |
Published version: | https://doi.org/10.1007/BFb0055655 |
Appears in Collections: | Aurora harvest 3 Australian Institute for Machine Learning publications Computer Science publications |
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