A variation of the neural algorithm locally excitatory globally inhibitory network has been developed for automatic recognition, count, and morphological analysis of a large number of quantum dots. In a typical image of 256 x 256 pixels (500 x 500 nm(2)), the program recognizes the dots with an average error of about 3%. It also calculates area and height of each of them. Furthermore, the recognition procedure is very fast (less than 2-3 s on a Pentium III at 600 MHz). (C) 2004 American Vacuum Society.
Application of a Segmentation Algorithm to Quantum Dots Study / Belardinelli, P., Capoleoni, S., Tirozzi, B., Coluzza, C.. - In: JOURNAL OF VACUUM SCIENCE AND TECHNOLOGY. B, NANOTECHNOLOGY & MICROELECTRONICS. - ISSN 2166-2746. - 22:2(2004), pp. 588-591. [10.1116/1.1651114]
Application of a Segmentation Algorithm to Quantum Dots Study
Belardinelli P;
2004-01-01
Abstract
A variation of the neural algorithm locally excitatory globally inhibitory network has been developed for automatic recognition, count, and morphological analysis of a large number of quantum dots. In a typical image of 256 x 256 pixels (500 x 500 nm(2)), the program recognizes the dots with an average error of about 3%. It also calculates area and height of each of them. Furthermore, the recognition procedure is very fast (less than 2-3 s on a Pentium III at 600 MHz). (C) 2004 American Vacuum Society.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
