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Probability mapping images in dynamic speckle classification |
Applied Optics, Vol. 52, Issue 4, pp. 726-733 (2013)
http://dx.doi.org/10.1364/AO.52.000726
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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.
© 2013 Optical Society of America
OCIS Codes
(030.6600) Coherence and statistical optics : Statistical optics
(110.6150) Imaging systems : Speckle imaging
(100.4993) Image processing : Pattern recognition, Baysian processors
ToC Category:
Scattering
History
Original Manuscript: July 5, 2012
Revised Manuscript: November 22, 2012
Manuscript Accepted: December 12, 2012
Published: January 30, 2013
Citation
Isabel Passoni, Héctor Rabal, Gustavo Meschino, and Marcelo Trivi, "Probability mapping images in dynamic speckle classification," Appl. Opt. 52, 726-733 (2013)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-52-4-726
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