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High-Order Differential Geometry of Curves for Multiview Reconstruction and Matching

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3757))

Abstract

The relationship between the orientation and curvature of projected curves and the orientation and curvature of the underlying space curve has been previously established. This has allowed a disambiguation of correspondences in two views and a transfer of these properties to a third view for confirmation. We propose that a higher-order intrinsic differential geometry attribute, namely, curvature derivative, is necessary to account for the range of variation of space curves and their projections. We derive relationships between curvature derivative in a projected view, and curvature derivative and torsion of the underlying space curve. Regardless of the point, tangent, and curvature, any pair of curvature derivatives are possible correspondences, but most would lead to very high torsion and curvature derivatives. We propose that the minimization of third order derivatives of the reconstruction, which combines torsion and curvature derivative of the space curve, regularizes the process of finding the correct correspondences.

This research is supported by NSF under grant 5.26422, and CNPq – Brazil Proc. 200875/2004-3 generously funded the first author’s activities.

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© 2005 Springer-Verlag Berlin Heidelberg

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Fabbri, R., Kimia, B.B. (2005). High-Order Differential Geometry of Curves for Multiview Reconstruction and Matching. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_42

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  • DOI: https://doi.org/10.1007/11585978_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30287-2

  • Online ISBN: 978-3-540-32098-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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