NDLI: Probability mapping images in dynamic speckle classification

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Trivi, Marcelo Passoni, Isabel Rabal, Héctor Meschino, Gustavo
Description	Author Affiliation: Passoni I ( Laboratorio de Bioingeniería, Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina. lpassoni@fi.mdp.edu.ar);
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.
ISSN	1559128X
Journal	Applied Optics
Issue Number	4
Volume Number	52
e-ISSN	21553165
Language	English
Publisher	Optical Society of America
Publisher Date	2013-02-01
Publisher Place	United States
Access Restriction	Subscribed
Subject Keyword	Algorithms Artificial Intelligence Colorimetry Methods Data Interpretation, Statistical Image Interpretation, Computer-Assisted Pattern Recognition, Automated Journal Article Research Support, Non-U.S. Gov't Optics
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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