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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 49, Iss. 19 — Jul. 1, 2010
  • pp: 3753–3761

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

Pablo Etchepareborda, Alejandro Federico, and Guillermo H. Kaufmann  »View Author Affiliations

Applied Optics, Vol. 49, Issue 19, pp. 3753-3761 (2010)

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We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi–Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

© 2010 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(120.3940) Instrumentation, measurement, and metrology : Metrology
(120.6150) Instrumentation, measurement, and metrology : Speckle imaging

ToC Category:
Instrumentation, Measurement, and Metrology

Original Manuscript: February 10, 2010
Manuscript Accepted: June 4, 2010
Published: June 25, 2010

Pablo Etchepareborda, Alejandro Federico, and Guillermo H. Kaufmann, "Sensitivity evaluation of dynamic speckle activity measurements using clustering methods," Appl. Opt. 49, 3753-3761 (2010)

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