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Article: Linear differential algorithm for motion recovery: a geometric approach

TitleLinear differential algorithm for motion recovery: a geometric approach
Authors
Issue Date2000
Citation
International Journal of Computer Vision, 2000, v. 36, n. 1, p. 71-89 How to Cite?
AbstractThe aim of this paper is to explore a linear geometric algorithm for recovering the three dimensional motion of a moving camera from image velocities. Generic similarities and differences between the discrete approach and the differential approach are clearly revealed through a parallel development of an analogous motion estimation theory previously explored in Vieville, T. and Faugeras, O.D. 1995. In Proceedings of Fifth International Conference on Computer Vision, pp. 750-756; Zhuang, X. and Haralick, R.M. 1984. In Proceedings of the First International Conference on Artificial Intelligence Applications, pp. 366-375. We present a precise characterization of the space of differential essential matrices, which gives rise to a novel eigenvalue-decomposition-based 3D velocity estimation algorithm from the optical flow measurements. This algorithm gives a unique solution to the motion estimation problem and serves as a differential counterpart of the well-known SVD-based 3D displacement estimation algorithm for the discrete case. Since the proposed algorithm only involves linear algebra techniques, it may be used to provide a fast initial guess for more sophisticated nonlinear algorithms (Ma et al., 1998c. Electronic Research Laboratory Memorandum, UC Berkeley, UCB/ERL(M98/37)). Extensive simulation results are presented for evaluating the performance of our algorithm in terms of bias and sensitivity of the estimates with respect to different noise levels in image velocity measurements.
Persistent Identifierhttp://hdl.handle.net/10722/326670
ISSN
2023 Impact Factor: 11.6
2023 SCImago Journal Rankings: 6.668
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMa, Y. I.-
dc.contributor.authorKošecká, Jana-
dc.contributor.authorSastry, Shankar-
dc.date.accessioned2023-03-31T05:25:40Z-
dc.date.available2023-03-31T05:25:40Z-
dc.date.issued2000-
dc.identifier.citationInternational Journal of Computer Vision, 2000, v. 36, n. 1, p. 71-89-
dc.identifier.issn0920-5691-
dc.identifier.urihttp://hdl.handle.net/10722/326670-
dc.description.abstractThe aim of this paper is to explore a linear geometric algorithm for recovering the three dimensional motion of a moving camera from image velocities. Generic similarities and differences between the discrete approach and the differential approach are clearly revealed through a parallel development of an analogous motion estimation theory previously explored in Vieville, T. and Faugeras, O.D. 1995. In Proceedings of Fifth International Conference on Computer Vision, pp. 750-756; Zhuang, X. and Haralick, R.M. 1984. In Proceedings of the First International Conference on Artificial Intelligence Applications, pp. 366-375. We present a precise characterization of the space of differential essential matrices, which gives rise to a novel eigenvalue-decomposition-based 3D velocity estimation algorithm from the optical flow measurements. This algorithm gives a unique solution to the motion estimation problem and serves as a differential counterpart of the well-known SVD-based 3D displacement estimation algorithm for the discrete case. Since the proposed algorithm only involves linear algebra techniques, it may be used to provide a fast initial guess for more sophisticated nonlinear algorithms (Ma et al., 1998c. Electronic Research Laboratory Memorandum, UC Berkeley, UCB/ERL(M98/37)). Extensive simulation results are presented for evaluating the performance of our algorithm in terms of bias and sensitivity of the estimates with respect to different noise levels in image velocity measurements.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Computer Vision-
dc.titleLinear differential algorithm for motion recovery: a geometric approach-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1023/A:1008124507881-
dc.identifier.scopuseid_2-s2.0-0343953399-
dc.identifier.volume36-
dc.identifier.issue1-
dc.identifier.spage71-
dc.identifier.epage89-
dc.identifier.isiWOS:000085086700004-

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