Abstract
T-CombNET neural network structure has obtained very good results in hand gesture recognition. However one of the most important setting is to define an input space that can optimize the global performance of this structure. In this paper the Interclass Distance Measurement criterion is analyzed and applied to select the space division in T-CombNET structure. The obtained results show that the use of the IDM criterion can improve the classification capability of the network when compared with practical approaches. Simulations using Japanese finger spelling has been done. The recognition rate has improved from 91.2% to 96.5% for dynamic hand motion recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Linde, Y., Buzo, A., and Gray, R. M.: An Algorithm for Vector Quantizers Design. IEEE Trans. on Communications, Vol. COM-28 (1980) 84–95
Young, T. Y., and Fu, K. S.: Handbook of Pattern Recognition and Image Processing, Academic Press (1986)
Jordan, M. I.: Serial order: A parallel distributed processing approach. Technical Report Nr. 8604, Institute for Cognitive Science, University of California, San Diego (1986)
Tamura, S., and Kawasaki, S.: Recognition of Sign Language Motion Images. Pattern Recognition, Vol.21 (1988) 343–353
Kohonen, T.: Improved Versions of Learning Vector Quantization. International Joint Conference on Neural Networks, San Diego (1990) 545–550
Elman, J. L.: Finding Structure in Time. Cognitive Science, 14, (1990) 179–221
Fukunaga, K.: Introduction to Statistical Pattern Recognition, Academic Press (1990)
Iwata, A., Suwa, Y., Ino, Y., and Suzumura, N.: Hand-Written Alpha-Numeric Recognition by a Self-Growing Neural Network: CombNET-II. Proceedings of the International Joint Conference on Neural Networks, Baltimore (1992)
Starner, T., and Pentland, A.: Visual Recognition of American Sign Language Using Hidden Markov Models. International Workshop on Automatic Face and Gesture Recognition, Zurich, Switzerland (1995)
Lamar, M. V., Bhuiyan, M. S., and Iwata, A.: Hand Gesture Recognition using Morphological PCA and an improved CombNET-II. Proceedings of the 1999 International Conference on System, Man, and Cybernetics, Vol.IV, Tokyo, Japan (1999) 57–62
Lamar, M. V., Bhuiyan, M. S., and Iwata, A.: Temporal Series Recognition using a new Neural Network structure T-CombNET. Proceedings of the 6th International Conference on Neural Information Processing, Vol.III, Perth, Australia (1999) 1112–1117
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lamar, M.V., Bhuiyan, M.S., Iwata1, A. (2000). T-CombNET - A Neural Network Dedicated to Hand Gesture Recognition. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_62
Download citation
DOI: https://doi.org/10.1007/3-540-45482-9_62
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67560-0
Online ISBN: 978-3-540-45482-3
eBook Packages: Springer Book Archive