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

Get Citation
Copy Citation Text
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)

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Get PDF (2305 KB)
Set citation alerts
Save article

Related Topics
Optics & Photonics Topics
?

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

Not Accessible

Your account may give you access

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.

Full Article | PDF Article

OSA Recommended Articles

Calibration of AOTF-based 3D measurement system using multiplane model based on phase fringe and BP neural network

Huijie Zhao, Shaoguang Shi, Hongzhi Jiang, Ying Zhang, and Zefu Xu
Opt. Express 25(9) 10413-10433 (2017)

Optimized star sensors laboratory calibration method using a regularization neural network

Chengfen Zhang, Yanxiong Niu, Hao Zhang, and Jiazhen Lu
Appl. Opt. 57(5) 1067-1074 (2018)

Generic nonsinusoidal fringe model and gamma calibration in phase measuring profilometry

Xu Zhang, Limin Zhu, Youfu Li, and Dawei Tu
J. Opt. Soc. Am. A 29(6) 1047-1058 (2012)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (9)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Equations (31)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

	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

Abstract

References

Cited By

Figures (9)

Tables (3)

Equations (31)

Metrics

Login or Create Account