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

Volume

51

Issue

5

fYear

2004

fDate

5/1/2004 12:00:00 AM

Firstpage

885

Lastpage

891

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;

fLanguage

English

Journal_Title

Circuits and Systems I: Regular Papers, IEEE Transactions on

Publisher

ieee

ISSN

1549-8328

Type

jour

DOI

10.1109/TCSI.2004.827637

Filename

1296801