Advanced Procrustes Analysis Models in Photogrammetric Computer Vision: 590 (CISM International Centre for Mechanical Sciences, 590) - Hardcover

Crosilla, Fabio; Beinat, Alberto; Fusiello, Andrea; Maset, Eleonora; Visintini, Domenico

Hardcover

ISBN 10: 3030117596 ISBN 13: 9783030117597

Publisher: Springer, 2019

Synopsis

This book gives a comprehensive view of the developed procrustes models, including the isotropic, the generalized and the anisotropic variants. These represent original tools to perform, among others, the bundle block adjustment and the global registration of multiple 3D LiDAR point clouds. Moreover, the book also reports the recently derived total least squares solution of the anisotropic Procrustes model, together with its practical application in solving the exterior orientation of one image. The book is aimed at all those interested in discovering valuable innovative algorithms for solving various photogrammetric computer vision problems. In this context, where functional models are non-linear, Procrustean methods prove to be powerful since they do not require any linearization nor approximated values of the unknown parameters, furnishing at the same time results comparable in terms of accuracy with those given by the state-of-the-art methods.

About this title

From the Back Cover

This book gives a comprehensive view of the developed procrustes models, including the isotropic, the generalized and the anisotropic variants. These represent original tools to perform, among others, the bundle block adjustment and the global registration of multiple 3D LiDAR point clouds. Moreover, the book also reports the recently derived total least squares solution of the anisotropic Procrustes model, together with its practical application in solving the exterior orientation of one image. The book is aimed at all those interested in discovering valuable innovative algorithms for solving various photogrammetric computer vision problems. In this context, where functional models are non-linear, Procrustean methods prove to be powerful since they do not require any linearization nor approximated values of the unknown parameters, furnishing at the same time results comparable in terms of accuracy with those given by the state-of-the-art methods.

Edition details

• Publisher: Springer
• Publication date: 2019
• Language: English
• ISBN 10: 3030117596
• ISBN 13: 9783030117597
• Binding: Hardcover
• Edition number: 1
• Number of pages: 183