Probability mapping images in dynamic speckle classification

We propose the use of a learning procedure to identify regions of similar dynamics in speckle image sequences that includes more than one descriptor. This procedure is based on the application of a naïve Bayes statistical classifier comprising the use of several descriptors. The class frontiers can be depicted so that the proportion of identified regions may be measured. To demonstrate the results, assembly of an RGB image, where each plane (R, G, and B) is associated with a particular region (class), was labeled according to its biospeckle dynamics. A high brightness in one color means a high probability of the pixel belonging to the corresponding class, and vice versa.Fil: Passoni, Isabel. Universidad Nacional de La Plata. Facultad de Ingenieria; ArgentinaFil: Rabal, Hector Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); ArgentinaFil: Meschino, Gustavo. Universidad Nacional de La Plata. Facultad de Ingenieria; ArgentinaFil: Trivi, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico La Plata. Centro de Investigaciones Opticas (i); Argentin