Bayesian Neural-Networks-Based Evaluation of Binary Speckle Data
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
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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