By Topic

Recognising aircraft: automatic extraction of structure by layers of quadratic neural nets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
McLaughlin, R.A. ; Centre for Intelligent Inf. Process. Syst., Western Australia Univ., Nedlands, WA, Australia ; Alder, M.D.

This paper explores a method of recognition applicable to a wide range of problems. It adopts a syntactic or structural approach, which involves viewing an object in terms of its simpler components. In the specific example of aircraft recognition, this entails decomposing an aircraft into entities such as a nose, fuselage, tail and wings. Each component may then be further decomposed as a set of pixels. The process of recognition requires finding sets of pixels forming such components and then sets of these components forming an aircraft. This is implemented by a quadratic neural network

Published in:

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on  (Volume:7 )

Date of Conference:

27 Jun-2 Jul 1994