Title :
Expert visual classification and neural networks: can general solutions be found?
Author :
Ellis, R. ; Simpson, R. ; Culverhouse, P.E. ; Parisini, T. ; Williams, R. ; Reguera, B. ; Moore, B. ; Lowe, D.
Author_Institution :
Dept. of Psychology, Plymouth Univ., Devon, UK
Abstract :
The authors discuss the potential of artificial neural networks for automating expert visual classifications undertaken in the marine sciences. They illustrate their application with two examples and show their performance, in those cases, to be strongly constrained by the nature of the training data. A proposal for escaping these data limitations is described which involves providing networks with multiple, coarse input channels. They provide evidence that performance is enhanced by this technique and suggest that it may be possible to construct networks which have a general ability to learn specific visual discriminations
Keywords :
expert systems; geophysical signal processing; image classification; neural nets; oceanographic techniques; remote sensing; artificial neural net; automating; expert visual classification; geophysical method measurement technique; image classification; neural network; ocean optical imaging; satellite remote sensing; sea; visible signal processing; visual discrimination; Application software; Artificial neural networks; Laboratories; Lipidomics; Liver; Marine animals; Marine vegetation; Microscopy; Neural networks; Psychology;
Conference_Titel :
OCEANS '94. 'Oceans Engineering for Today's Technology and Tomorrow's Preservation.' Proceedings
Conference_Location :
Brest
Print_ISBN :
0-7803-2056-5
DOI :
10.1109/OCEANS.1994.363867