Integration of high resolution spatial and spectral data acquisition systems for monitoring purposes in cultural heritage applications

The concern and interest of this PhD thesis is the registration of featureless 3D and multispectral datasets describing cultural heritage objects.In this context, there are few natural salient features between the complementary datasets, and the use of targets is generally proscribed.We thus develop a technique based on the photogrammetric tracking of the acquisition systems in use.A series of simulations was performed to evaluate the accuracy of our method in three configurations chosen to represent a variety of cultural heritage objects.These simulations show that we can achieve a spatial tracking accuracy of 0.020 mm and an angular accuracy of 0.100 mrad using four 5 Mpx cameras when digitizing an area of 400 mm x 700 mm. The accuracy of the final registration relies on the success of a series of optical and geometrical calibrations and their stability for the duration of the full acquisition process.The accuracy of the tracking and registration was extensively tested in laboratory settings. We first evaluated the potential for multiview 3D registration. Then, the method was used for to project of multispectral images on 3D models.Finally, we used the registered data to improve the reflectance estimation from the multispectral datasets

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Source https://theses.hal.science/tel-00909743
Author Simon Chane, Camille, Simon
Maintainer CCSD
Last Updated May 8, 2026, 02:07 (UTC)
Created May 8, 2026, 02:07 (UTC)
Identifier NNT: 2013DIJOS008
Language en
Rights https://about.hal.science/hal-authorisation-v1/
contributor Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i) ; Université de Bourgogne (UB)-École Nationale Supérieure d'Arts et Métiers (ENSAM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)
creator Simon Chane, Camille, Simon
date 2013-03-26T00:00:00
harvest_object_id 74a81027-3be9-4d97-b42a-dd7e9c3f3775
harvest_source_id 3374d638-d20b-4672-ba96-a23232d55657
harvest_source_title test moissonnage SELUNE
metadata_modified 2026-03-31T00:00:00
set_spec type:THESE