Abstract
In this paper we present a neural architecture for a mental imaging like generation of image sequences. Mental imaging plays a central role for various perception processes. Thereto, we investigated mechanisms to model this ability of biological systems at a functional level for sequences of images. Because it is impossible to memorize many experienced sequences, we developed an universal, general and very powerful approach based on the ability to predict optic flow fields as consequences of the systems own actions and tested the resulting architecture on a real mobile system.
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Stephan, V., Gross, HM. (2001). Neural Architecture for Mental Imaging of Sequences Based on Optical Flow Predictions. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_122
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DOI: https://doi.org/10.1007/3-540-44668-0_122
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