Summary
In this paper we present a novel software framework for efficient representation and manipulations of tensors which aims in minimizing data copying. Tensors are stored in the matricized form with simultaneous abstraction superimposed on tensor indices thanks to the proxy design pattern. The proposed software pattern was then used in computation of the Higher- Order Singular Value Decomposition. Finally, the whole framework was tested in the problem of static gesture recognition.
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Cyganek, B. (2010). Software Framework for Efficient Tensor Representation and Decompositions for Pattern Recognition in Computer Vision. In: Choraś, R.S. (eds) Image Processing and Communications Challenges 2. Advances in Intelligent and Soft Computing, vol 84. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16295-4_21
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DOI: https://doi.org/10.1007/978-3-642-16295-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16294-7
Online ISBN: 978-3-642-16295-4
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