Image pre-processing for optimizing automated photogrammetry performances

The purpose of this paper is to analyze how optical pre-processing with polarizing filters and digital pre-processing with HDR imaging, may improve the automated 3D modeling pipeline based on SFM and Image Matching, with special emphasis on optically non-cooperative surfaces of shiny or dark materia...

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Main Authors: G. Guidi, S. Gonizzi, L. L. Micoli
Format: Article
Language:English
Published: Copernicus Publications 2014-05-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/145/2014/isprsannals-II-5-145-2014.pdf
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spelling doaj-4c3df0f119f7407e8fef50f9633a17352020-11-24T20:49:15ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502014-05-01II-514515210.5194/isprsannals-II-5-145-2014Image pre-processing for optimizing automated photogrammetry performancesG. Guidi0S. Gonizzi1L. L. Micoli2Department of Mechanical Engineering, Politecnico di Milano, via la Masa 1, 20156, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, via la Masa 1, 20156, ItalyDepartment of Mechanical Engineering, Politecnico di Milano, via la Masa 1, 20156, ItalyThe purpose of this paper is to analyze how optical pre-processing with polarizing filters and digital pre-processing with HDR imaging, may improve the automated 3D modeling pipeline based on SFM and Image Matching, with special emphasis on optically non-cooperative surfaces of shiny or dark materials. Because of the automatic detection of homologous points, the presence of highlights due to shiny materials, or nearly uniform dark patches produced by low reflectance materials, may produce erroneous matching involving wrong 3D point estimations, and consequently holes and topological errors on the mesh originated by the associated dense 3D cloud. This is due to the limited dynamic range of the 8 bit digital images that are matched each other for generating 3D data. The same 256 levels can be more usefully employed if the actual dynamic range is compressed, avoiding luminance clipping on the darker and lighter image areas. Such approach is here considered both using optical filtering and HDR processing with tone mapping, with experimental evaluation on different Cultural Heritage objects characterized by non-cooperative optical behavior. Three test images of each object have been captured from different positions, changing the shooting conditions (filter/no-filter) and the image processing (no processing/HDR processing), in order to have the same 3 camera orientations with different optical and digital pre-processing, and applying the same automated process to each photo set.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/145/2014/isprsannals-II-5-145-2014.pdf
collection DOAJ
language English
format Article
sources DOAJ
author G. Guidi
S. Gonizzi
L. L. Micoli
spellingShingle G. Guidi
S. Gonizzi
L. L. Micoli
Image pre-processing for optimizing automated photogrammetry performances
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
author_facet G. Guidi
S. Gonizzi
L. L. Micoli
author_sort G. Guidi
title Image pre-processing for optimizing automated photogrammetry performances
title_short Image pre-processing for optimizing automated photogrammetry performances
title_full Image pre-processing for optimizing automated photogrammetry performances
title_fullStr Image pre-processing for optimizing automated photogrammetry performances
title_full_unstemmed Image pre-processing for optimizing automated photogrammetry performances
title_sort image pre-processing for optimizing automated photogrammetry performances
publisher Copernicus Publications
series ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
issn 2194-9042
2194-9050
publishDate 2014-05-01
description The purpose of this paper is to analyze how optical pre-processing with polarizing filters and digital pre-processing with HDR imaging, may improve the automated 3D modeling pipeline based on SFM and Image Matching, with special emphasis on optically non-cooperative surfaces of shiny or dark materials. Because of the automatic detection of homologous points, the presence of highlights due to shiny materials, or nearly uniform dark patches produced by low reflectance materials, may produce erroneous matching involving wrong 3D point estimations, and consequently holes and topological errors on the mesh originated by the associated dense 3D cloud. This is due to the limited dynamic range of the 8 bit digital images that are matched each other for generating 3D data. The same 256 levels can be more usefully employed if the actual dynamic range is compressed, avoiding luminance clipping on the darker and lighter image areas. Such approach is here considered both using optical filtering and HDR processing with tone mapping, with experimental evaluation on different Cultural Heritage objects characterized by non-cooperative optical behavior. Three test images of each object have been captured from different positions, changing the shooting conditions (filter/no-filter) and the image processing (no processing/HDR processing), in order to have the same 3 camera orientations with different optical and digital pre-processing, and applying the same automated process to each photo set.
url http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5/145/2014/isprsannals-II-5-145-2014.pdf
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