DocumentCode :
2286070
Title :
An analogic CNN-algorithm of pixel level snakes for tracking and surveillance tasks
Author :
Vilarino, D.L. ; Cabello, Diego ; Brea, Victor M.
Author_Institution :
Dept. of Electron. & Comput. Sci., Santiago de Compostela Univ., Spain
fYear :
2002
fDate :
22-24 Jul 2002
Firstpage :
84
Lastpage :
91
Abstract :
This paper addresses the application of the pixel level snakes for the segmentation of moving objects. This kind of active contour techniques can handle multiple contours simultaneously without time-processing penalty as well as to manage appropriately the topologic transformations among them when this is required. The implementation into a CNNUM or a specific purpose CNN platform gives solution to the speed requirements of this kind of tasks. Particularly, we show an analogic CNN-algorithm which meets all the constrains imposed for the current CNNUM hardware implementations.
Keywords :
cellular neural nets; image segmentation; image sequences; surveillance; tracking; CNNUM; active contour techniques; analogic CNN-algorithm; cellular neural nets; moving object segmentation; multiple contours; pixel level snakes; surveillance tasks; time-processing penalty; tracking tasks; Active contours; Application software; Cellular neural networks; Computer science; Deformable models; Hardware; Image segmentation; Image sequences; Layout; Surveillance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cellular Neural Networks and Their Applications, 2002. (CNNA 2002). Proceedings of the 2002 7th IEEE International Workshop on
Print_ISBN :
981-238-121-X
Type :
conf
DOI :
10.1109/CNNA.2002.1035039
Filename :
1035039
Link To Document :
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