Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

ETCHEPAREBORDA, P.; FEDERICO, A.; KAUFMANN, G. H.

APPLIED OPTICS

OPTICAL SOC AMER

Lugar: Washington, DC; Año: 2010 vol. 49 p. 3753 - 3761

0003-6935

The use of competitive neural networks, self organizing maps, the expectation-maximization algorithm, K-Means and fuzzy C-Means techniques as partitional clustering methods are evaluated and compared 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 the clustering purpose. 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.