Abstract
As shown in the previous chapters, given a matrix \(\mathbf {A}\) (origin) and a matrix \(\mathbf {B}\) (destination), containing the coordinates of p-points in \(\mathbb {R}^k\), classical least squares (LS) Procrustes solutions find the transformation parameters between the two point sets assuming that all random errors are confined to the destination matrix \(\mathbf {B}\), whereas \(\mathbf {A}\) is noise-free. However, this assumption is often unrealistic, since both \(\mathbf {A}\) and \(\mathbf {B}\) are corrupted by errors if they derive from measurements.
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© 2019 CISM International Centre for Mechanical Sciences
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Crosilla, F., Beinat, A., Fusiello, A., Maset, E., Visintini, D. (2019). Procrustes Errors-In-Variables Solutions. In: Advanced Procrustes Analysis Models in Photogrammetric Computer Vision. CISM International Centre for Mechanical Sciences, vol 590. Springer, Cham. https://doi.org/10.1007/978-3-030-11760-3_5
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DOI: https://doi.org/10.1007/978-3-030-11760-3_5
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