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Evaluation of laser dynamic speckle signals applying granular computing.

, , and . Signal Process., 89 (3): 266-274 (2009)

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Unsupervised learning segmentation for dynamic speckle activity images., , , , , , and . CoRR, (2014)Characterization of Viability of Seeds by Using Dynamic Speckles and Difference Histograms., , , and . CIARP, volume 2905 of Lecture Notes in Computer Science, page 329-333. Springer, (2003)Decision and classification problems using Mahalanobis statistical distance., , , and . FSKD, page 1012-1016. IEEE, (2011)Signal Feature Extraction Using Granular Computing. Comparative Analysis with Frequency and Time Descriptors Applied to Dynamic Laser Speckle Patterns., , , , and . Int. J. Comput. Intell. Syst., 8 (sup2): 28-40 (2015)Evaluation of speckle-interferometry descriptors to measuring drying-of-coatings., , , and . Signal Process., 91 (10): 2395-2403 (2011)Improvements in the Visualization of Segmented Areas of Patterns of Dynamic Laser Speckle., , , , , , , and . WSOM, volume 198 of Advances in Intelligent Systems and Computing, page 163-171. Springer, (2012)Evaluation of laser dynamic speckle signals applying granular computing., , and . Signal Process., 89 (3): 266-274 (2009)Characterization of a paint drying process through granulometric analysis of speckle dynamic patterns., , , , and . Signal Process., 90 (5): 1623-1630 (2010)