A parallel network for visual cognition

Author

Adams, Frank W., Jr. ; Nguyen, H.T. ; Raghavan, Raghu ; Slawny, Joseph

Author_Institution

Lockheed Palo Alto Res. Lab., CA, USA

Volume

3

Issue

6

fYear

1992

fDate

11/1/1992 12:00:00 AM

Firstpage

906

Lastpage

922

Abstract

The authors describe a parallel dynamical system designed to integrate model-based and data-driven approaches to image recognition in a neural network, and study one component of the system in detail. That component is the translation-invariant network of probabilistic cellular automata (PCA), which combines feature-detector outputs and collectively performs enhancement and recognition functions. Recognition is a novel application of the PCA. Given a model of the target object, conditions on the PCA weights are obtained which must be satisfied for object enhancement and noise rejection to occur, and engineered weights are constructed. For further refinement of the weights, a training algorithm derived from optimal control theory is proposed. System operation is illustrated with examples derived from visual, infrared, and laser-radar imagery

Keywords

automata theory; cognitive systems; computer vision; image recognition; neural nets; optimal control; parallel processing; IR imagery; data-driven approaches; feature-detector; image enhancement; image recognition; laser-radar imagery; model based approach; neural network; noise rejection; optimal control theory; parallel dynamical system; parallel network; probabilistic cellular automata; training algorithm; visual cognition; Cognition; Image recognition; Inference algorithms; Infrared imaging; Laboratories; Laser feedback; Laser modes; Laser radar; Neural networks; Principal component analysis;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.165593

Filename

165593