Title :
A new object-oriented segmentation algorithm based on CNNs - part II: performance evaluation
Author :
Grassi, Giuseppe ; Di Sciascio, Eugenio ; Grieco, Alfredo L. ; Vecchio, Pietro
Author_Institution :
Dipt. Ingegneria Innovazione, Universita di Lecce, Italy
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.
Keywords :
cellular neural nets; edge detection; image segmentation; object detection; performance evaluation; cellular neural network universal machine; image contours; object-oriented segmentation; performance evaluation; Cellular neural networks; Computer networks; Filtering; Gray-scale; Hardware; Image processing; Image segmentation; MPEG 4 Standard; Object oriented modeling; Video sequences;
Conference_Titel :
Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
Print_ISBN :
0-7803-9185-3
DOI :
10.1109/CNNA.2005.1543183