title: Sensitivity evaluation of dynamic speckle activity measurements using clustering methods.
abstract: 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.
authors.affiliation: Electrónica e Informática, Instituto Nacional de Tecnología Industrial, P.O. Box B1650WAB, B1650KNA San Martín, Argentina. pabloe@inti.gov.ar
authors.forename: Pablo
authors.initials: P
authors.lastname: Etchepareborda
authors.forename: Alejandro
authors.initials: A
authors.lastname: Federico
authors.forename: Guillermo H
authors.initials: GH
authors.lastname: Kaufmann