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Advanced Procrustes Analysis Models in Photogrammetric Computer Vision

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advanced Procrustes Analysis Models in Photogrammetric Computer Vision/ by Fabio Crosilla, Alberto Beinat, Andrea Fusiello, Eleonora Maset, Domenico Visintini.
Author:	Crosilla, Fabio.
other author:	Beinat, Alberto.
Description:	XI, 172 p. 66 illus., 59 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Remote sensing. -
Online resource:	https://doi.org/10.1007/978-3-030-11760-3
ISBN:	9783030117603

Advanced Procrustes Analysis Models in Photogrammetric Computer Vision
Crosilla, Fabio.

Advanced Procrustes Analysis Models in Photogrammetric Computer Vision[electronic resource] /by Fabio Crosilla, Alberto Beinat, Andrea Fusiello, Eleonora Maset, Domenico Visintini. - 1st ed. 2019. - XI, 172 p. 66 illus., 59 illus. in color.online resource. - CISM International Centre for Mechanical Sciences, Courses and Lectures,5900254-1971 ;. - CISM International Centre for Mechanical Sciences, Courses and Lectures,559.

Theory of procrustes analysis models -- An introduction to computer vision and laser scanning -- Applications of procrustes analysis models.

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.

ISBN: 9783030117603

Standard No.: 10.1007/978-3-030-11760-3doiSubjects--Topical Terms:

557272
Remote sensing.

LC Class. No.: GA102.4.R44

Dewey Class. No.: 910.285

Advanced Procrustes Analysis Models in Photogrammetric Computer Vision
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