Summary
In this paper the generalization capabilities of the Hierarchical Temporal Memory (HTM), the new computing paradigm based on cortical theory, has been exploited in order to recognize the hand shape in real images while training is done on synthetically generated data. It has been shown that the HTM trained in this way and tuned up with a small number of real examples gives pretty good recognition rates. Additionally the good scalability of the proposed solution has been observed while analyzing the recognition rates for the class that is ’almost unknown’ because only few examples are shown during training.
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Kapuściński, T. (2010). Hand Shape Recognition in Real Images Using Hierarchical Temporal Memory Trained on Synthetic Data. 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_22
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DOI: https://doi.org/10.1007/978-3-642-16295-4_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16294-7
Online ISBN: 978-3-642-16295-4
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