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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 43, Iss. 28 — Oct. 1, 2004
  • pp: 5356–5363

Bayesian neural-networks-based evaluation of binary speckle data

Udo V. Toussaint, Silvio Gori, and Volker Dose  »View Author Affiliations


Applied Optics, Vol. 43, Issue 28, pp. 5356-5363 (2004)
http://dx.doi.org/10.1364/AO.43.005356


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Abstract

We present a new method using Bayesian probability theory and neural networks for the evaluation of speckle interference patterns for an automated analysis of deformation and erosion measurements. The method is applied to the fringe pattern reconstruction of speckle measurements with a Twyman-Green interferometer. Given a binary speckle image, the method returns the fringe pattern without noise, thus removing the need for smoothing and allowing a straightforward unwrapping procedure and determination of the surface shape. Because no parameters have to be adjusted, the method is especially suited for continuous and automated monitoring of surface changes.

© 2004 Optical Society of America

OCIS Codes
(100.2650) Image processing : Fringe analysis
(100.5010) Image processing : Pattern recognition
(120.6160) Instrumentation, measurement, and metrology : Speckle interferometry

History
Original Manuscript: February 5, 2004
Revised Manuscript: June 25, 2004
Published: October 1, 2004

Citation
Udo V. Toussaint, Silvio Gori, and Volker Dose, "Bayesian neural-networks-based evaluation of binary speckle data," Appl. Opt. 43, 5356-5363 (2004)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-43-28-5356

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