Abstract
The problem of estimating motion fields from image se- quences is essential for robot vision and so on. This paper discusses a method for estimating an entire continuous motion-vector field from a given set of image-sequence data. One promising method to realize accurate and efficient estimations is to fuse different estimation methods. We propose a neural network-based method to estimate motion-vector fields. The proposed method fuses two conventional methods, the correlation method and the differential method by model inclusive learning, which enables approximation results to possess inherent property of vector fields. It is shown through experiments that the proposed method makes it possible to estimate motion fields more accurately.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jahne, B.: Digital Image Processing, 6th edn. Springer, Heidelberg (2005)
Mussa-Ivaldi, F.A.: From basis functions to basis fields: vector field approximation from sparse data. Biological Cybernetics 67, 479–489 (1992)
Kuroe, Y., Kawakami, H.: Vector field approximation by model inclusive learning of neural networks. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D.P. (eds.) ICANN 2007. LNCS, vol. 4668, pp. 717–726. Springer, Heidelberg (2007)
Kuroe, Y., Nakai, Y., Mori, T.: A Learning Method of Nonlinear Mappings by Neural Networks with Considering Their Derivatives. In: Proc. IJCNN, Nagoya, Japan, pp. 528–531 (1993)
Luenberger, D.G.: Introduction to Linear and Nonlinear Programming, pp. 194–197. Addison-Wesley, Reading (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kuroe, Y., Kawakami, H. (2009). Estimation Method of Motion Fields from Images by Model Inclusive Learning of Neural Networks. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_68
Download citation
DOI: https://doi.org/10.1007/978-3-642-04277-5_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
Online ISBN: 978-3-642-04277-5
eBook Packages: Computer ScienceComputer Science (R0)