Abstract
Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. Recently, the neurally-inspired algorithmic lateral inhibition (ALI) method and its application to the motion detection task have been introduced. The article shows how to implement the tasks directly related to ALI in motion detection by means of a formal model described as finite state machines. Automata modeling is the first step towards real-time implementation by FPGAs and programming of ”intelligent” camera processors.
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Mira, J., Delgado, A.E., Fernández-Caballero, A., López, M.T., Fernández, M.A. (2007). The Underlying Formal Model of Algorithmic Lateral Inhibition in Motion Detection. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_14
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DOI: https://doi.org/10.1007/978-3-540-73055-2_14
Publisher Name: Springer, Berlin, Heidelberg
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