Share Email Print

Proceedings Paper

Color and shape classification with competing paradigms: neural networks versus trainable table classifiers
Author(s): Robert Charles Massen; Thomas Regle; Pia Boettcher
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 pixel-wise classification of CCD colour Images Into previously learned colour classes at video-rate is a demanding vision task, both with regard to the complicated cluster shapes encountered in natui- al scenes and to the required computing power for real-time operation. We discuss two classical solutions based on an algorithmic statistical classifier and on a Neural Network paradigm and propose an alternative simple and low-cost classifier based on approbriately trained look-up-tables. Two different learningrules for the supervised training of this LUT classifier are presented for the colour classification of both synthetic and natural blotechno1ojr scenes. The proposed LUT classifier shows all the positive features of a (simulated) 3-layer perceptron Neural Network, but performs 60.000 times faster with simple, commercially available components.

Paper Details

Date Published: 1 August 1990
PDF: 12 pages
Proc. SPIE 1265, Industrial Inspection II, (1 August 1990); doi: 10.1117/12.20237
Show Author Affiliations
Robert Charles Massen, Transfer Ctr. Constance for Image Data Processing (Germany)
Thomas Regle, Transfer Ctr. Constance for Image Data Processing (Germany)
Pia Boettcher, Transfer Ctr. Constance for Image Data Processing (Germany)

Published in SPIE Proceedings Vol. 1265:
Industrial Inspection II
Donald W. Braggins, Editor(s)

© SPIE. Terms of Use
Back to Top