Using Neural Networks to Detect Microfossil Teeth in Somosaguas Sur Paleontological Site

  • R. Gil-Pita
  • N. Sala-Burgos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4224)


Automatic microfossil detection system allows to extract the position of the microfossils in a concentrate of mineral grains, speeding up the time required to analyze each sample. In this paper we study the use of Multilayer Perceptrons and Radial Basis Function Networks applied to the automatic microfossil teeth detection problem, focusing on the dependence of the performance with the size of the network, and with the size of the training set. The data used in the experiments are three images of concentrates with micromammal teeth from Somosaguas paleontological site, in Madrid (Spain). The obtained results demonstrate RBFNs perform better than MLPs in most of the considered cases, detecting most of the microfossil teeth in the images.


Radial Basis Function Training Image Middle Miocene Radial Basis Function Network Hide Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bollmann, J., Quinn, P., Vela, M., Brabec, B., Brechner, S., Corts, M.Y., Hilbrecht, H., Schmidt, D.N., Schiebel, R., Thierstein, H.R.: Automated particle analysis: calcareous microfossils. In: Image Analysis, Sediments and Paleoenvironments. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  2. Sala-Burgos, N., Gil-Pita, R.: Automatic Microfossil Detection in Somosaguas Sur paleontologic site (Pozuelo de Alarcon, Madrid, Spain) using Multilayer Perceptrons. WSEAS Trans. on Signal Processing 2(2), 218–223 (2006)Google Scholar
  3. Luis, A., Hernando, J.M.: The microvertebrates of the Middle Miocene of Somosaguas Sur (Pozuelo de Alarcón, Madrid, Spain). Coloquios de Paleontología 51, 87–136 (2000)Google Scholar
  4. Mínguez Gandú, D.: Marco estratigráfico y sedimentológico de los yacimientos Miocenos de Somosaguas (Madrid, Spain). Coloquios de Paleontología 51, 183–196 (2000)Google Scholar
  5. López Martinez, N., Élez Villar, J., Hernando Hernando, J.M., Luis Cavia, A., Mazo, A., Minguez Gandú, D., Morales, J., Polonio Martín, I., Salesa, M.J., Snchez, I.M.: The fossil vertebrates from Somosaguas (Pozuelo, Madrid, Spain). Coloquios de Paleontología 51, 69–85 (2000)Google Scholar
  6. López Martínez, N.: Técnicas de Estudio de Microvertebrados. Los micromamíferos y su interés bioestratigráfico. Paleontología de Vertebrados. Faunas y filogenia, aplicación y sociedad, Ed. Univ. País Vasco, 345–365 (1992)Google Scholar
  7. Van Trees, H.L.: Detection, estimation, and modulation theory, 1st edn. Wiley, Chichester (1968)zbMATHGoogle Scholar
  8. Ruck, D.W., Rogers, S.K., Kabrisky, M., Oxley, M.E., Suter, B.W.: The multilayer Perceptron as an approximation to a Bayes optimal discriminant function. IEEE Transactions on Neural Networks 1(1), 296–298 (1990)CrossRefGoogle Scholar
  9. Hagan, M.T., Menhaj, M.B.: Training Feedforward Networks with the Marquardt Algorithm. IEEE Trans. on Neural Networks 5(6), 989–993 (1994)CrossRefGoogle Scholar
  10. MacKay, D.J.C.: Bayesian interpolation. Neu. Comp. 4, 415–447 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • R. Gil-Pita
    • 1
    • 2
  • N. Sala-Burgos
    • 1
    • 2
  1. 1.Departamento de Teoría de la Señal y ComunicacionesUniversidad de AlcaláSpain
  2. 2.Departamento de PaleontologíaUniversidad Complutense de MadridSpain

Personalised recommendations