Abstract
We present the “Parametrized Self-Organizing Map” (PSOM) as a method for 3D object recognition and pose estimation. The PSOM can be seen as a continuous extension of the standard Self-Organizing Map which generalizes the discrete set of reference vectors to a continuous manifold. In the context of visual learning, manifolds based on PSOMs can be used to represent the appearance of various objects. We demonstrate this approach and its merits in an application example.
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References
J.L. Crowley and F. Pourraz. Continuity properties of the appearance manifold formobile robot position estimation. Image and Vision Computing, 2001.
Teuvo Kohonen. Self-Organizing maps. Springer series in information science. Springer, Berlin, Heidelberg, New York, third edition, 2001.
H. Murase and S.K. Nayar. Visual learning and recognition of 3-d objects from appearance. Int’l J. Computer Vision, 14:5–24, 1995.
S. A. Nene, S. K. Nayar, and H. Murase. Columbia Object Image Library (COIL-20). Technical Report CUCUS-006-96, Dept. Computer Science, Columbia Univ. New York, N.Y. 10027, 1996.
C. Nölker and H. Ritter. Parametrized SOMs for hand posture reconstruction. In S.-I. Amari, C.L. Giles, M. Gori, and V. Piuri, editors, Proc. IEEE-INNS-ENNS Int’l Joint Conf. on Neural Networks IJCNNY’2000, 2000.
T.D. Sanger. Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks, 2:459–473, 1989.
Michael A. Sipe and David Casasent. Global feature space neural network for active object recognition. In IJCNN’99, Washington, D.C., 1999.
Jörg Walter and Helge Ritter. Local PSOMs and Chebyshev PSOMs-improving the parametrised self-organizing maps. In Proc. ICANN, Paris, volume 1, pages 95–102, October 1995.
Jörg Walter and Helge Ritter. Rapid learning with parametrized self-organizing maps. Neurocomputing, 12:131–153, 1996.
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© 2002 Springer-Verlag Berlin Heidelberg
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Saalbach, A., Heidemann, G., Ritter, H. (2002). Parametrized SOMs for Object Recognition and Pose Estimation. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_146
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DOI: https://doi.org/10.1007/3-540-46084-5_146
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