Multisensor object segmentation using a neural network

Patrick T. Gaughan; Gerald M. Flachs; Jay B. Jordan

doi:10.1117/12.45019

1 August 1991 Multisensor object segmentation using a neural network

Patrick T. Gaughan, Gerald M. Flachs, Jay B. Jordan

Proceedings Volume 1469, Applications of Artificial Neural Networks II; (1991) https://doi.org/10.1117/12.45019
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

A neural network architecture is presented to segment objects using multiple sensor/feature images. The neural architecture consists of a region growing net to separate an object from the surrounding background based upon local statistical properties. The region growing net consists of a lattice of neural processing elements for propagating a similarity activity between image pixels. A potential function approach is presented to define the neural weights by measuring pixel similarity in multisensor/feature images. The performance of the neural segmenter is demonstrated by comparing its performance to that of an architecture using a statistical decision theoretic technique.

Citation Download Citation

Patrick T. Gaughan, Gerald M. Flachs, and Jay B. Jordan "Multisensor object segmentation using a neural network", Proc. SPIE 1469, Applications of Artificial Neural Networks II, (1 August 1991); https://doi.org/10.1117/12.45019

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available