Spectral Identification -- Summary of main findings to date


Pilot
European
Image
Processing
Archive


The PCCV Project:
Benchmarking Vision Systems

Overview
Tutorials
Methodology
Case studies
Test datasets
Our image file format
HATE test harness



Information
General links
Conferences
Mailing lists
Research groups
Societies

Techniques (CVonline)
Software
Image databases


Other stuff
Linux on ThinkPad

The main findings of our work are given very briefly below. Detailed descriptions of our work and findings can be found in our publications.

  • Since correlation is unable to deal with test spectra which comprise combinations of template spectra, this method was not considered suitable for this type of work.

  • The numerical optimization examined here was also not found to be satisfactory.

  • SVD was superior to any method in dealing with test spectra that were linearly-related to the template data. Unfortunately, it performed worse than both the genetic algorithms and neural networks for non-linearly related cases. While the iterative procedure devised by us greatly improved matters, it was still not as good as the neural networks. The SVD method has the added advantage of speed, test cases can be run almost instantaneously, saving a considerable amount of time.

  • Neural networks were found to be good, especially for noisy test data; no other method was superior in the presence of noise. However, for larger datasets the training time became an issue.

  • Genetic algorithms were found to be good for small datasets (less than 20 spectra). However, analysis of the results needs more careful consideration than any of the other methods. For example, normalized test and template data had to be treated in a different way to the equivalent non-normalized cases.

  • We have shown that both SVD and neural networks can be used effectively with large databases. Scaling up the number of template spectra had some effects on the neural network, most probably due to inability to train it to a small enough error. However, SVD seemed unperturbed: provided the number of bins describing a spectrum is greater than the number of template spectra, the performance appears to be unaffected. The results from the neural network and SVD were complementary: where the neural network gave poor results, SVD gave good results and vice versa. Hence, these results suggest that, an optimum spectral identification system would be a neural network/SVD hybrid.


Last updated on 16-Jan-2004 by Christine Clark. [Comment on this page]