Abstract
This work present the use of a neural structure to augment the quality of noisy images of liquid bridges to obtain a clear representation of its border in order to determine the acceleration that it is suffering. The used network is a three layers Discrete Time Cellular Neural Network in which the last one performs the contour highlighting through the adaptive definition of the gain and threshold of their output functions. Then an easy algorithm extracts a curve from the border. © Springer-Verlag Berlin Heidelberg 2001.
Cite
CITATION STYLE
Jaramillo, M. A., Fernández, J. Á., Montanero, J. M., & Zayas, F. (2001). Image quality enhancement for liquid bridge parameter estimation with DTCNN. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2085 LNCS, pp. 246–253). Springer Verlag. https://doi.org/10.1007/3-540-45723-2_29
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