Neural networks applied to measurement of three-dimensional object shapes using phases recovered from Fourier transform profilometry and phase locked loops

Dinesh Ganotra; Joby Joseph; Kehar Singh

doi:10.1117/12.530079

19 November 2003 Neural networks applied to measurement of three-dimensional object shapes using phases recovered from Fourier transform profilometry and phase locked loops

Dinesh Ganotra, Joby Joseph, Kehar Singh

Author Affiliations +

Proceedings Volume 4829, 19th Congress of the International Commission for Optics: Optics for the Quality of Life; (2003) https://doi.org/10.1117/12.530079
Event: 19th Congress of the International Commission for Optics: Optics for the Quality of Life, 2002, Florence, Italy

Abstract

Calibration in fringe projection profilometry is investigated using neural networks (NNs) and several calibration planes whose positions in space are known. Radial basis function (RBF) based- and backpropagation neural networks (BPNNs) are compared for phase-to-depth conversion for phase planes calculated using Fourier transform profilometry (FTP) and phase locked loops (PLLs). Experimental results are presented.

Citation Download Citation

Dinesh Ganotra, Joby Joseph, and Kehar Singh "Neural networks applied to measurement of three-dimensional object shapes using phases recovered from Fourier transform profilometry and phase locked loops", Proc. SPIE 4829, 19th Congress of the International Commission for Optics: Optics for the Quality of Life, (19 November 2003); https://doi.org/10.1117/12.530079

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