Title :
Implementation of a pixel-level snake algorithm on a CNNUM-based chip set architecture
Author :
Vilariño, David L. ; Rekeczky, Csaba
Author_Institution :
Dept. de Electron. y Comput., Santiago de Compostela Univ., Spain
fDate :
5/1/2004 12:00:00 AM
Abstract :
In this paper, an on-chip implementation of the active contour technique called pixel-level snakes is proposed. This is based on an optimized cellular neural network (CNN) algorithm with capabilities to support changes in the contour topology. The entire algorithm has been implemented on a 64×64 CNN universal machine chip-set architecture for which the results of the time performance measurements are given. To illustrate the validity and capabilities of the proposed implementation some on-chip experiments are also included.
Keywords :
cellular neural nets; image segmentation; image sequences; CNNUM-based chip; active contour technique; active contours; analogic algorithms; cellular neural network algorithm; contour topology; on-chip implementation; pixel-level snake algorithm; set architecture; time performance measurements; universal machine chip-set architecture; Active contours; Automation; Biomembranes; Cellular neural networks; Elasticity; Image segmentation; Measurement; Network topology; Shape control; Turing machines; Active contours; CNN universal machine; CNNUM; CNNs; PLS; analogic algorithms; cellular neural networks; pixel-level snakes;
Journal_Title :
Circuits and Systems I: Regular Papers, IEEE Transactions on
DOI :
10.1109/TCSI.2004.827637
