DocumentCode
2560647
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
fYear
2005
fDate
28-30 May 2005
Firstpage
150
Lastpage
153
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
Print_ISBN
0-7803-9185-3
Type
conf
DOI
10.1109/CNNA.2005.1543183
Filename
1543183
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