Constraint networks in vision

Author

Suter, David

Author_Institution

Dept. of Comput. Sci. & Comput. Eng., La Trobe Univ., Bundoora, Vic., Australia

Volume

40

Issue

12

fYear

1991

fDate

12/1/1991 12:00:00 AM

Firstpage

1359

Lastpage

1367

Abstract

Applications in machine vision of constraint networks based on an augmented Lagrangian formulation are discussed. Only those applications that have a fundamental significance are addressed. The first of these provides a generalization of the Harris coupled depth-slope analog model of visual reconstruction. Because of the generality of the approach, one can derive many more alternative structures, and the mathematical setting places this approach within the bounds of mixed finite element theory. This offers many advantages in terms of the associated mathematical theory and implementation on digital machines. The second use is in data fusion, which is a crucial task for systems using multiple sensors or methods of analysis of data

Keywords

computer vision; finite element analysis; neural nets; Harris coupled depth-slope analog model; associated mathematical theory; augmented Lagrangian formulation; constraint networks; data fusion; finite element theory; machine vision; multiple sensors; neural networks; visual reconstruction; Analog computers; Application software; Computer networks; Computer vision; Finite element methods; Intelligent networks; Lagrangian functions; Layout; Neural networks; Surface reconstruction;

fLanguage

English

Journal_Title

Computers, IEEE Transactions on

Publisher

ieee

ISSN

0018-9340

Type

jour

DOI

10.1109/12.106221

Filename

106221