Background intensity removal in structured light three-dimensional reconstruction
In Fourier Transform Profilometry, a filtering procedure is performed to separate the desired information (first order spectrum) from other unwanted contributions such as the background component (zero-order spectrum). However, if the zero-order spectrum and the high order spectra component interfer...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2016
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8976
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8976
- Palabra clave:
- Contour measurement
Image processing
Information filtering
Mathematical morphology
Mathematical transformations
Profilometry
Vision
Background components
Bi-dimensional empirical mode decompositions
Filtering procedures
Fourier transform profilometry
High order spectra
Morphological operations
Sequential combination
Three-dimensional reconstruction
Signal processing
- Rights
- restrictedAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
id |
UTB2_a9db33cab3b9bc50a37489210ee1f664 |
---|---|
oai_identifier_str |
oai:repositorio.utb.edu.co:20.500.12585/8976 |
network_acronym_str |
UTB2 |
network_name_str |
Repositorio Institucional UTB |
repository_id_str |
|
dc.title.none.fl_str_mv |
Background intensity removal in structured light three-dimensional reconstruction |
title |
Background intensity removal in structured light three-dimensional reconstruction |
spellingShingle |
Background intensity removal in structured light three-dimensional reconstruction Contour measurement Image processing Information filtering Mathematical morphology Mathematical transformations Profilometry Vision Background components Bi-dimensional empirical mode decompositions Filtering procedures Fourier transform profilometry High order spectra Morphological operations Sequential combination Three-dimensional reconstruction Signal processing |
title_short |
Background intensity removal in structured light three-dimensional reconstruction |
title_full |
Background intensity removal in structured light three-dimensional reconstruction |
title_fullStr |
Background intensity removal in structured light three-dimensional reconstruction |
title_full_unstemmed |
Background intensity removal in structured light three-dimensional reconstruction |
title_sort |
Background intensity removal in structured light three-dimensional reconstruction |
dc.contributor.editor.none.fl_str_mv |
Altuve M. |
dc.subject.keywords.none.fl_str_mv |
Contour measurement Image processing Information filtering Mathematical morphology Mathematical transformations Profilometry Vision Background components Bi-dimensional empirical mode decompositions Filtering procedures Fourier transform profilometry High order spectra Morphological operations Sequential combination Three-dimensional reconstruction Signal processing |
topic |
Contour measurement Image processing Information filtering Mathematical morphology Mathematical transformations Profilometry Vision Background components Bi-dimensional empirical mode decompositions Filtering procedures Fourier transform profilometry High order spectra Morphological operations Sequential combination Three-dimensional reconstruction Signal processing |
description |
In Fourier Transform Profilometry, a filtering procedure is performed to separate the desired information (first order spectrum) from other unwanted contributions such as the background component (zero-order spectrum). However, if the zero-order spectrum and the high order spectra component interfere the fundamental spectra, the 3D reconstruction precision decreases. In this paper, we test two recently proposed methods for removing the background intensity so as to improve Fourier Transform Profilometry reconstruction precision. The first method is based on the twice piece-wise Hilbert transform. The second is based on Bidimensional Empirical Mode Decomposition, but the decomposition is carried out by morphological operations In this work, we present as a novel contribution, the sequential combination of these two methods for removing the background intensity and other unwanted frequencies close to the first order spectrum, thus obtaining the 3D topography of the object. Encouraging experimental results show the advantage of the proposed method. © 2016 IEEE. |
publishDate |
2016 |
dc.date.issued.none.fl_str_mv |
2016 |
dc.date.accessioned.none.fl_str_mv |
2020-03-26T16:32:42Z |
dc.date.available.none.fl_str_mv |
2020-03-26T16:32:42Z |
dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_c94f |
dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
dc.type.hasVersion.none.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.spa.none.fl_str_mv |
Conferencia |
status_str |
publishedVersion |
dc.identifier.citation.none.fl_str_mv |
2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 |
dc.identifier.isbn.none.fl_str_mv |
9781509037971 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/8976 |
dc.identifier.doi.none.fl_str_mv |
10.1109/STSIVA.2016.7743326 |
dc.identifier.instname.none.fl_str_mv |
Universidad Tecnológica de Bolívar |
dc.identifier.reponame.none.fl_str_mv |
Repositorio UTB |
dc.identifier.orcid.none.fl_str_mv |
57117284600 57192270016 24329839300 36142156300 |
identifier_str_mv |
2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 9781509037971 10.1109/STSIVA.2016.7743326 Universidad Tecnológica de Bolívar Repositorio UTB 57117284600 57192270016 24329839300 36142156300 |
url |
https://hdl.handle.net/20.500.12585/8976 |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.relation.conferencedate.none.fl_str_mv |
30 August 2016 through 2 September 2016 |
dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_16ec |
dc.rights.uri.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
dc.rights.accessRights.none.fl_str_mv |
info:eu-repo/semantics/restrictedAccess |
dc.rights.cc.none.fl_str_mv |
Atribución-NoComercial 4.0 Internacional |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/4.0/ Atribución-NoComercial 4.0 Internacional http://purl.org/coar/access_right/c_16ec |
eu_rights_str_mv |
restrictedAccess |
dc.format.medium.none.fl_str_mv |
Recurso electrónico |
dc.format.mimetype.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers Inc. |
publisher.none.fl_str_mv |
Institute of Electrical and Electronics Engineers Inc. |
dc.source.none.fl_str_mv |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85002980258&doi=10.1109%2fSTSIVA.2016.7743326&partnerID=40&md5=da38e732664072c4c61c6bb0219286df Scopus2-s2.0-85002980258 |
institution |
Universidad Tecnológica de Bolívar |
dc.source.event.none.fl_str_mv |
21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 |
bitstream.url.fl_str_mv |
https://repositorio.utb.edu.co/bitstream/20.500.12585/8976/1/MiniProdInv.png |
bitstream.checksum.fl_str_mv |
0cb0f101a8d16897fb46fc914d3d7043 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 |
repository.name.fl_str_mv |
Repositorio Institucional UTB |
repository.mail.fl_str_mv |
repositorioutb@utb.edu.co |
_version_ |
1814021688989319168 |
spelling |
Altuve M.Vargas R.Pineda J.Marrugo A.G.Romero L.A.2020-03-26T16:32:42Z2020-03-26T16:32:42Z20162016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 20169781509037971https://hdl.handle.net/20.500.12585/897610.1109/STSIVA.2016.7743326Universidad Tecnológica de BolívarRepositorio UTB57117284600571922700162432983930036142156300In Fourier Transform Profilometry, a filtering procedure is performed to separate the desired information (first order spectrum) from other unwanted contributions such as the background component (zero-order spectrum). However, if the zero-order spectrum and the high order spectra component interfere the fundamental spectra, the 3D reconstruction precision decreases. In this paper, we test two recently proposed methods for removing the background intensity so as to improve Fourier Transform Profilometry reconstruction precision. The first method is based on the twice piece-wise Hilbert transform. The second is based on Bidimensional Empirical Mode Decomposition, but the decomposition is carried out by morphological operations In this work, we present as a novel contribution, the sequential combination of these two methods for removing the background intensity and other unwanted frequencies close to the first order spectrum, thus obtaining the 3D topography of the object. Encouraging experimental results show the advantage of the proposed method. © 2016 IEEE.Universidad Pontificia Bolivariana (UPB) Seccional BucaramangaRecurso electrónicoapplication/pdfengInstitute of Electrical and Electronics Engineers Inc.http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/restrictedAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_16echttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85002980258&doi=10.1109%2fSTSIVA.2016.7743326&partnerID=40&md5=da38e732664072c4c61c6bb0219286dfScopus2-s2.0-8500298025821st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016Background intensity removal in structured light three-dimensional reconstructioninfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionConferenciahttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_c94fContour measurementImage processingInformation filteringMathematical morphologyMathematical transformationsProfilometryVisionBackground componentsBi-dimensional empirical mode decompositionsFiltering proceduresFourier transform profilometryHigh order spectraMorphological operationsSequential combinationThree-dimensional reconstructionSignal processing30 August 2016 through 2 September 2016Federico, A., Kaufmann, G.H., Phase retrieval in digital speckle pattern interferometry by use of a smoothed space-frequency distribution (2003) Applied Optics, 42 (35), pp. 7066-7071Ǵomez, A.L.G., Fonseca, J.E.M., Téllez, J.L., Proyeccíon de franjas en metroloǵa optica facial (2012) INGE CUC, 8 (1), pp. 191-206Guan, C., Hassebrook, L., Lau, D., Composite structured light pattern for three-dimensional video (2003) Optics Express, 11 (5), pp. 406-417Guo, H., (2009) 3-D Shape Measurement Based on Fourier Transform and Phase Shifting Method, , PhD thesis, STATE UNIVERSITY OF New York AT STONY BROOKHuang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Liu, H.H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis (1998) Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454 (1971), pp. 903-995. , MarKemao, Q., Windowed fourier transform for fringe pattern analysis (2004) Applied Optics, 43 (13), pp. 2695-2702Luo, F., Chen, W., Su, X., Eliminating zero spectra in fourier transform profilometry by application of hilbert transform (2016) Optics Communications, 365, pp. 76-85Nunes, J.C., Bouaoune, Y., Delechelle, E., Niang, O., Bunel, P., Image analysis by bidimensional empirical mode decomposition (2003) Image and Vision Computing, 21 (12), pp. 1019-1026Su, X., Chen, W., Fourier transform profilometry:: A review (2001) Optics and Lasers in EngineeringSu, X., Chen, W., Reliability-guided phase unwrapping algorithm: A review (2004) Optics and Lasers in Engineering, 42 (3), pp. 245-261Takeda, M., Mutoh, K., Fourier transform profilometry for the automatic measurement of 3-d object shapes (1983) Applied Optics, 22 (24), pp. 3977-3982Villa, J., Araiza, M., Alaniz, D., Ivanov, R., Ortiz, M., Transformation of phase to (x, y, z)-coordinates for the calibration of a fringe projection profilometer (2012) Optics and Lasers in Engineering, 50 (2), pp. 256-261Zhong, J., Weng, J., Dilating gabor transform for the fringe analysis of 3-d shape measurement (2004) Optical Engineering, 43 (4), pp. 895-899Zhong, J., Weng, J., Spatial carrier-fringe pattern analysis by means of wavelet transform: Wavelet transform profilometry (2004) Applied Optics, 43 (26), pp. 4993-4998Zhou, X., Podoleanu, A.G., Yang, Z., Yang, T., Zhao, H., Morphological operation-based bi-dimensional empirical mode decomposition for automatic background removal of fringe patterns (2012) Optics Express, 20 (22), pp. 24247-24262http://purl.org/coar/resource_type/c_c94fTHUMBNAILMiniProdInv.pngMiniProdInv.pngimage/png23941https://repositorio.utb.edu.co/bitstream/20.500.12585/8976/1/MiniProdInv.png0cb0f101a8d16897fb46fc914d3d7043MD5120.500.12585/8976oai:repositorio.utb.edu.co:20.500.12585/89762021-02-02 14:49:25.597Repositorio Institucional UTBrepositorioutb@utb.edu.co |