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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/74vjt6olkvfqpdykhombmbjp74" style="color: black;">Engineering applications of artificial intelligence</a>
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. In the few last years, the neurally inspired lateral inhibition in accumulative computation (LIAC) method and its application to the motion detection task have been introduced. The article shows how to<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.engappai.2009.08.006">doi:10.1016/j.engappai.2009.08.006</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q545oajvhffkfizzyxz6obkbc4">fatcat:q545oajvhffkfizzyxz6obkbc4</a> </span>
more »... t the tasks directly related to LIAC in motion detection by means of a formal model described as finite state machines. This paper introduces two steps towards that direction: (a) A simplification of the general LIAC method is performed by formally transforming it into a finite state machine. (b) A hardware implementation of such a designed LIAC module, as well as an 8 Â 8 LIAC module, has been tested on several video sequences, providing promising performance results.
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