title: Probability mapping images in dynamic speckle classification.
abstract: 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.
authors: Laboratorio de Bioingeniería, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina. lpassoni@fi.mdp.edu.ar
authors: Isabel
authors: I
authors: Passoni
authors: Héctor
authors: H
authors: Rabal
authors: Gustavo
authors: G
authors: Meschino
authors: Marcelo
authors: M
authors: Trivi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods*; D003627:Data Interpretation, Statistical; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*