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...

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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/
Description
Summary: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.