TY - BOOK ID - 46189010 TI - Advanced Procrustes Analysis Models in Photogrammetric Computer Vision AU - Crosilla, Fabio. AU - Beinat, Alberto. AU - Fusiello, Andrea. AU - Maset, Eleonora. AU - Visintini, Domenico. PY - 2019 SN - 303011760X 3030117596 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Photogrammetry KW - Digital techniques. KW - Digital photogrammetric systems KW - Digital photogrammetry KW - DP (Digital photogrammetry) KW - DPS (Digital photogrammetric systems) KW - Digital electronics KW - Computer vision. KW - Remote Sensing/Photogrammetry. KW - Image Processing and Computer Vision. KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Image processing KW - Pattern recognition systems KW - Remote sensing. KW - Optical data processing. KW - Optical computing KW - Visual data processing KW - Bionics KW - Electronic data processing KW - Integrated optics KW - Photonics KW - Computers KW - Remote-sensing imagery KW - Remote sensing systems KW - Remote terrain sensing KW - Sensing, Remote KW - Terrain sensing, Remote KW - Aerial photogrammetry KW - Aerospace telemetry KW - Detectors KW - Space optics KW - Optical equipment KW - Geographic information systems. KW - Geographical Information System. KW - Computer Vision. KW - Geographical information systems KW - GIS (Information systems) KW - Information storage and retrieval systems KW - Geography UR - https://www.unicat.be/uniCat?func=search&query=sysid:46189010 AB - 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. ER -