Pattern recognition system with top-down process of mental rotation

  • Shunji Satoh
  • Hirotomo Aso
  • Shogo Miyake
  • Jousuke Kuroiwa
Artificial Intelligence and Cognitive Neuroscience
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1606)


A new model which can recognize rotated, distorted, scaled, shifted and noised patterns is proposed. The model is constructed based on psychological experiments in a mental rotation. The model has two types of processes: (i) one is a bottom-up process in which pattern recognition is realized by means of a rotation-invariant neocognitron and a standard neocognitron and (ii) the other is a top-down process in which a mental rotation is executed by means of a model of associative recall in visual pattern recognition. In computer simulations, it is shown that the model can recognize rotated patterns without training those patterns.


rotation-invariant neocognitron rotated pattern mental rotation top-down process pattern recognition 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. Fukumi, S. Omatu, and Y. Nishikawa, “Rotation-Invariant Neural Pattern Recognition System Estimating a Rotation Angle”, IEEE Trans. Neural Network, Vol. 8, pp. 568–581, 1997.CrossRefGoogle Scholar
  2. 2.
    M. B. Reid, L. Spirkovska, and E. Ochoa, “Rapid training of higher order neural networks for invariant pattern recognition,” Proc. Int. Joint Conf. Neural Networks, Vol. 1, pp. 689–692, 1989.Google Scholar
  3. 3.
    B. Widrow, R. G. Winter and R. A. Baxter, “Layered neural nets for pattern recognition,” IEEE Trans. Acoust., Speech, Signal Processing, Vol. 36, pp. 1109–1118, 1988.CrossRefMATHGoogle Scholar
  4. 4.
    S. Satoh, J. Kuroiwa, H. Aso and S. Miyake, “Recognition of rotated patterns using neocognitron,” Proc. Int. Conf. Neural Information Processing, Vol. 1, pp. 112–116, 1997. Scholar
  5. 5.
    S. Satoh, J. Kuroiwa, H. Aso and S. Miyake, “A rotation-invariant Neocognitron (in Japanese),” IEICE Trans., Vol.J81-DII, 1998.Google Scholar
  6. 6.
    K. Fukushima, “Neocognitron: A hierarchical neural network capable of visual pattern recognition,” Neural Networks, Vol. 1, No. 2, pp. 119–130, 1988.MathSciNetCrossRefGoogle Scholar
  7. 7.
    R.N. Shepard and J. Metzler, “Mental rotation of three-dimensional object,” Science, Vol. 171, pp. 701–703, 1971.CrossRefGoogle Scholar
  8. 8.
    C. Stanfill and D. Waltz, “Toward memory-based reasoning,” Communication ACM, Vol. 29, pp. 1213–1228, 1986.CrossRefGoogle Scholar
  9. 9.
    K. Fukushima and N. Wake, “An improved learning algorithm for the neocognitron,” Proc. of the Int. Conf. on Artificial Neural Networks, pp. 4–7 (1992).Google Scholar
  10. 10.
    S. Satoh, J. Kuroiwa, H. Aso and S. Miyake, “Recognition of hand-written patterns by rotation-invariant neocognitron,” Proc. Int. Conf. Neural Information Processing, Vol. 1, pp. 295–299, 1998. Scholar
  11. 11.
    K. Fukushima, “Neural network model for selective attention in visual pattern recognition and associative recall,” Applied Optics, Vol.26, pp. 4985–4992, 1987.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Shunji Satoh
    • 1
  • Hirotomo Aso
    • 1
  • Shogo Miyake
    • 2
  • Jousuke Kuroiwa
    • 3
  1. 1.Department of Electrical CommunicationsTohoku UniversitySendaiJapan
  2. 2.Department of Applied PhysicsTohoku UniversitySendaiJapan
  3. 3.The Division of Mathematical and Information SciencesHiroshima UniversityJapan

Personalised recommendations