Abstract
In this paper, in aid of ANN ensemble, a supervised online learning strategy continuously achieves omnidirectional information accumulation for 3D object cognition from 2D view sequence. The notion of similarity is introduced to solve the paradox between information simplicity and accuracy. Images are segmented into homogeneous region for training, correspondent to distinct model views characteristic of neighboring generalization. Real-time techniques are adopted to expand knowledge until satisfactory. The insert into joint model views is only needed in case of impartibility. Simulation experiment has achieved encouraging results, and proved the approach effective and feasible.
The National 863 Natural Science Foundation of P. R. China (2001AA635010) fully supported this research.
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References
Wang, S.J.: Biomimetics pattern recognition. INNS, ENNS, JNNS Newletters Elseviers (2003)
Cyr, C.M., Kimia, B.B.: A Similarity-Based Aspect-Graph Approach to 3D Object Recognition. International Journal of Computer Vision 57(1) (2004)
Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition, pp. 97–99. Publishing House of Electronic Industry of China, Beijing (1996)
Wang, S.J., Wang, B.N.: Analysis and theory of high-dimension spatial geometry for Artificial Neural Networks. Acta Electronica Sinica 30(1), 1–4 (2002)
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© 2005 Springer-Verlag Berlin Heidelberg
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Nian, R., Ji, G., Zhao, W., Feng, C. (2005). ANN Ensemble Online Learning Strategy in 3D Object Cognition and Recognition Based on Similarity. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_106
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DOI: https://doi.org/10.1007/11539087_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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