By using the CNN paradigm, this paper and the companion one (Grasi et al., 2005) present a new object-oriented segmentation algorithm, which takes into account the hardware characteristics imposed by the CNNUM. In particular, by exploiting a rigorous model of the image contours, this paper focuses on CNN algorithms for edge extraction. Simulation results show that the approach provides more accurate edge extractions than the ones obtained by other CNN-based techniques.
A New Object-oriented Segmentation Algorithm based on CNNs - Part I: Edge Extraction / Grassi, G.; Di Sciascio, E.; Grieco, Luigi Alfredo; Vecchio, P.. - (2005), pp. 158-161. (Intervento presentato al convegno 9th IEEE International Workshop on Cellular Neural Networks and their Applications, CNNA tenutosi a Hsinchu, Taiwan nel May 28-30, 2005) [10.1109/CNNA.2005.1543185].
A New Object-oriented Segmentation Algorithm based on CNNs - Part I: Edge Extraction
Di Sciascio, E.;GRIECO, Luigi Alfredo;
2005-01-01
Abstract
By using the CNN paradigm, this paper and the companion one (Grasi et al., 2005) present a new object-oriented segmentation algorithm, which takes into account the hardware characteristics imposed by the CNNUM. In particular, by exploiting a rigorous model of the image contours, this paper focuses on CNN algorithms for edge extraction. Simulation results show that the approach provides more accurate edge extractions than the ones obtained by other CNN-based techniques.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
