By using the CNN paradigm, this paper illustrates a new object-oriented segmentation algorithm that takes into account the hardware characteristics imposed by the CNNUM. In particular, this paper describes every block of the algorithm except the edge extraction one, which is described in the companion paper (Grasi et al., 2005). Additionally, by considering different video sequences, this paper illustrates some performance evaluations, showing that the approach (based on a rigorous model of the image contours) provides more accurate segmented objects than the ones obtained by other CNN-based techniques.
A New Object-oriented Segmentation Algorithm based on CNNs - Part II: Performance Evaluation / Grassi, G.; Sciascio, E. D.; Grieco, Luigi Alfredo; Vecchio, P.. - (2005), pp. 150-153. (Intervento presentato al convegno 9th International Workshop on Cellular Neural Networks and Their Applications, CNNA 2005 tenutosi a Hsinchu, Taiwan nel May 28-30, 2005) [10.1109/CNNA.2005.1543183].
A New Object-oriented Segmentation Algorithm based on CNNs - Part II: Performance Evaluation
GRIECO, Luigi Alfredo;
2005-01-01
Abstract
By using the CNN paradigm, this paper illustrates a new object-oriented segmentation algorithm that takes into account the hardware characteristics imposed by the CNNUM. In particular, this paper describes every block of the algorithm except the edge extraction one, which is described in the companion paper (Grasi et al., 2005). Additionally, by considering different video sequences, this paper illustrates some performance evaluations, showing that the approach (based on a rigorous model of the image contours) provides more accurate segmented objects 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.
