Share Email Print

Proceedings Paper

Pulse-coupled neural network shadow compensation
Author(s): John L. Johnson; Jaime R. Taylor; Matthew Anderson
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The Pulsed Coupled Neural Network (PCNN) algorithm, when modified for use as an image processor, provides a unique method of multiplicative image decomposition (PCNN factorization). Because the factorization is ordered by levels of scene contrast, the first few factors contain the strong contrasts generally associated with shadows. The PCNN factorization effectively and automatically finds scene shadows. This is further developed here as a computationally effective shadow compensation algorithm with illustrative examples given, and is shown to be significantly more effective than histogram equalization. The advantage and disadvantages are discussed.

Paper Details

Date Published: 22 March 1999
PDF: 5 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342901
Show Author Affiliations
John L. Johnson, U.S. Army Aviation and Missile Command (Germany)
Jaime R. Taylor, Austin Peay State Univ. (United States)
Matthew Anderson, Austin Peay State Univ. (United States)

Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

© SPIE. Terms of Use
Back to Top