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Image of Minimal rotations in arbitrary dimensions with applications to hypothesis testing and parameter estimation

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Minimal rotations in arbitrary dimensions with applications to hypothesis testing and parameter estimation

Jochen Meidow - Nama Orang; Horst Hammer - Nama Orang;

The rotation of a vector around the origin and in a plane constitutes a minimal rotation. Such a rotation is of vital importance in many applications. Examples are the re-orientation of spacecraft or antennas with minimal effort, the smooth interpolation between sensor poses, and the drawing of circular arcs in 2D and 3D. In numerical linear algebra, minimal rotations in different planes are used to manipulate matrices, e.g., to compute the QR decomposition of a matrix. This review compiles the concepts and formulas for minimal rotations in arbitrary dimensions for easy reference and provides a summary of the mathematical background necessary to understand the techniques described in this paper. The discussed concepts are accompanied by important examples in the context of photogrammetric image analysis. Hypothesis testing and parameter estimation for uncertain geometric entities are described in detail. In both applications, minimal rotations play an important role.


Ketersediaan
74621.3678Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
621.3678
Penerbit
Amsterdam : Elsevier., 2025
Deskripsi Fisik
12 hlm PDF, 1.279 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
621.3678
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.15, January 2025
Subjek
Rotation
Parameter estimation
Hypersphere
Homogeneous coordinates
Info Detail Spesifik
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Pernyataan Tanggungjawab
-
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Tidak tersedia versi lain

Lampiran Berkas
  • Minimal rotations in arbitrary dimensions with applications to hypothesis testing and parameter estimation
    The rotation of a vector around the origin and in a plane constitutes a minimal rotation. Such a rotation is of vital importance in many applications. Examples are the re-orientation of spacecraft or antennas with minimal effort, the smooth interpolation between sensor poses, and the drawing of circular arcs in 2D and 3D. In numerical linear algebra, minimal rotations in different planes are used to manipulate matrices, e.g., to compute the QR decomposition of a matrix. This review compiles the concepts and formulas for minimal rotations in arbitrary dimensions for easy reference and provides a summary of the mathematical background necessary to understand the techniques described in this paper. The discussed concepts are accompanied by important examples in the context of photogrammetric image analysis. Hypothesis testing and parameter estimation for uncertain geometric entities are described in detail. In both applications, minimal rotations play an important role.
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