Advertisement

Neurocomputing in Space

  • Bipin Kumar Tripathi
Chapter
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 571)

Abstract

The high-dimensional neural network is becoming very popular in almost every intelligence system design, just to name few, computer vision, robotics, biometric identification, control, communication system, and forecasting are the scientific and engineering fields that take advantage of artificial neural networks (ANN) to emulate intelligent behavior. In computer vision the interpretation of 3D motion, 3D transformations, and 3D face or object recognition are important tasks. There have been many methodologies to solve them, but these methods are time consuming and weak to noise. The advantage of using neural networks for object recognition is the feasibility of a training system to capture the complex class conditional density of patterns. It will be desirable to explore the capabilities of ANN that can directly process three-dimensional information. This article discusses the machine learning from the view points of 3D vector-valued neural network and corresponding applications. The learning and generalization capacity of high-dimensional ANN is confirmed through diverse simulation examples.

Keywords

Artificial Neural Network Point Cloud Face Recognition High Dimensional Mapping Class Conditional Density 
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.
This is a preview of subscription content, log in to check access.

References

  1. 1.
    Tripathi, B.K., Kalra, P.K.: The novel aggregation function based neuron models in complex domain. Soft Comput. 14(10), 1069–1081. Springer, (2010)Google Scholar
  2. 2.
    Tripathi, B.K., Kalra, P.K.: On the learning machine for three dimensional mapping. Neural Comput. Appl. 20(01), 105–111. Springer, (2011)Google Scholar
  3. 3.
    Oh, B.J.: Face recognition by using neural network classifiers based on PCA and LDA. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 1699–1703, 10–12 Oct 2005Google Scholar
  4. 4.
    Zhou, X., Bhanu, B.: Integrating face and gait for human recognition at a distance video. IEEE Trans. Syst. Man Cybern. 37(5), 1119–1137 (2007)CrossRefGoogle Scholar
  5. 5.
    Pantic, M., Patras, I.: Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences. IEEE Trans. Syst. Man Cybern. 36(2), 433–449 (2007)CrossRefGoogle Scholar
  6. 6.
    Hietmeyer, R.: Biometric identification promises fast and secure processing of airline passengers. Int. Civil Aviat. Organ. J. 55(9), 10–11 (2000)Google Scholar
  7. 7.
    O’Tolle, A.J., Abdi, H., Jiang, F., Phillips, P.J.: Fusing face-verification algorithms and humans. IEEE Trans. Syst. Man Cybern. 37(5), 1149–1155 (2007)Google Scholar
  8. 8.
    Blanz, V., Vetter, T.: Face recognition based on fitting a 3D morphable model. IEEE Trans. PAMI 25(9), 1063–1074 (2003)CrossRefGoogle Scholar
  9. 9.
    Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: Letters 2D and 3D face recognition: a survey. Pattern Recognit. 28, 1885–1906 (2007)CrossRefGoogle Scholar
  10. 10.
    Tripathi, B.K., Kalra, P.K.: On efficient learning machine with root power mean neuron in complex domain. IEEE Trans. Neural Network 22(05), 727–738 (2011). ISSN: 1045–9227Google Scholar
  11. 11.
    Nitta, T.: An analysis of the fundamental structure of complex-valued neurons. Neural Process. Lett. 12, 239–246 (2000)CrossRefMATHGoogle Scholar
  12. 12.
    Piazza, F., Benvenuto, N.: On the complex backpropagation algorithm. IEEE Trans. Signal Process. 40(4), 967–969 (1992)CrossRefGoogle Scholar
  13. 13.
    Tripathi, B.K., Kalra, P.K.: Complex generalized-mean neuron model and its applications. Appl. Soft Comput. 11(01), 768–777. Elsevier Science, (2011)Google Scholar
  14. 14.
    Nitta, T.: An extension of the back-propagation algorithm to complex numbers. Neural Networks 10(8), 1391–1415 (1997)CrossRefGoogle Scholar
  15. 15.
    Moreno, A.B., Sanchez, A., Velez, J.F., Daz, F.J.: Face recognition using 3D surface-extracted descriptors. In: Proceedings of the IMVIPC (2003)Google Scholar
  16. 16.
    Achermann, B., Bunke, H.: Classifying range images of human faces with Hausdorff distance. In: Proceedings of the ICPR, pp. 809–813 (2000)Google Scholar
  17. 17.
    Xu, C., Wang, Y., Tan, T., Quan, L.: Automatic 3D face recognition combining global geometric features with local shape variation information. In: Proceedings of the AFGR, pp. 308–313 (2004)Google Scholar
  18. 18.
    Lee, C.C., Chung, P.C., Tsai, J.R., Chang, C.I.: Robust radial basis function neural network. IEEE Trans. Syst. Man Cybern. B Cybern. 29(6), 674–685 (1999)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Computer Science and EngineeringHarcourt Butler Technological InstituteKanpurIndia

Personalised recommendations