Neural network digital fringe calibration technique for structured light profilometers

Matthew J. Baker; Jiangtao Xi; Joe F. Chicharo

doi:10.1364/AO.46.001233

Neural network digital fringe calibration technique for structured light profilometers

Matthew J. Baker, Jiangtao Xi, and Joe F. Chicharo

Applied Optics
Vol. 46,
Issue 8,
pp. 1233-1243
(2007)
•https://doi.org/10.1364/AO.46.001233

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Matthew J. Baker, Jiangtao Xi, and Joe F. Chicharo, "Neural network digital fringe calibration technique for structured light profilometers," Appl. Opt. 46, 1233-1243 (2007)

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Optics & Photonics Topics
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The topics in this list come from the OSA Optics and Photonics Topics applied to this article.
Previously assigned OCIS codes
- Fringe analysis (120.2650)
- Surface measurements, figure (120.6650)
- Three-dimensional sensing (150.6910)

About this Article
History
- Original Manuscript: July 24, 2006
- Manuscript Accepted: September 25, 2006
- Published: February 20, 2007

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Abstract

We present a novel neural network signal calibration technique to improve the performance of triangulation-based structured light profilometers based on digital projection. The performance of such profilometers is often hindered by the capture of aberrated pattern intensity distributions, and hence we address this problem by employing neural networks in a signal mapping approach. We exploit the generalization and interpolation capabilities of a feed-forward backpropagation neural network to map from distorted fringe data to nondistorted data. The performance of the calibration technique is gauged both through simulation and experimentation, with simulation results indicating that accuracy can be improved by more than 80%. The technique requires just one image cross section for calibration and hence is ideal for rapid profiling applications.

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	PMP (N = 3) $ϵ_{e}$ (mm)	$σ_{e}$	FTP $ϵ_{e}$ (mm)	$σ_{e}$
Noncalibrated	5.9393	5.5648	6.7831	5.4483
Calibrated	0.7072	0.5853	1.1054	0.9284

Distortion	PMP				FTP
	Calibrated		Noncalibrated		Calibrated		Noncalibrated
	$ϵ_{e}$ (mm)	$σ_{e}$	$ϵ_{e}$ (mm)	$σ_{e}$	$ϵ_{e}$ (mm)	$σ_{e}$	$ϵ_{e}$ (mm)	$σ_{e}$
Gamma	0.0042	0.0045	3.4803	3.6952	0.4338	0.4055	4.1730	3.6315
Harmonic	0.0012	0.0013	1.6308	1.7326	0.4337	0.4053	2.1559	1.8686
Lens	0.1497	0.1662	0.0690	0.0911	0.4136	0.3916	0.3593	0.3464

Technique	$ϵ_{e}$ (mm)	$σ_{e}$ (mm)
Noncalibrated	5.1349	5.4465
Neural Network	0.1773	0.1890
10 tap low-pass filter	5.0283	5.3274
100 tap low-pass filter	0.5250	0.5986

	PMP (N = 3) $ϵ_{e}$ (mm)	$σ_{e}$	FTP $ϵ_{e}$ (mm)	$σ_{e}$
Noncalibrated	5.9393	5.5648	6.7831	5.4483
Calibrated	0.7072	0.5853	1.1054	0.9284

Distortion	PMP				FTP
	Calibrated		Noncalibrated		Calibrated		Noncalibrated
	$ϵ_{e}$ (mm)	$σ_{e}$	$ϵ_{e}$ (mm)	$σ_{e}$	$ϵ_{e}$ (mm)	$σ_{e}$	$ϵ_{e}$ (mm)	$σ_{e}$
Gamma	0.0042	0.0045	3.4803	3.6952	0.4338	0.4055	4.1730	3.6315
Harmonic	0.0012	0.0013	1.6308	1.7326	0.4337	0.4053	2.1559	1.8686
Lens	0.1497	0.1662	0.0690	0.0911	0.4136	0.3916	0.3593	0.3464

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