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Real-time motion detection by lateral inhibition in accumulative computation.

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dc.contributor.author López Bonal, María Teresa es_ES
dc.contributor.author Fernández Caballero, Antonio es_ES
dc.contributor.author Delgado García, Ana. E es_ES
dc.date.accessioned 2012-03-20T07:10:56Z
dc.date.available 2012-03-20T07:10:56Z
dc.date.issued 2010 es_ES
dc.identifier.citation Engineering applications of artificial intelligence, 2010, 23(1): 129-139 es_ES
dc.identifier.issn 0952-1976 es_ES
dc.identifier.uri http://hdl.handle.net/10578/2095
dc.description.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. 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 implement 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 8×8 LIAC module, has been tested on several video sequences, providing promising performance results. es_ES
dc.format text/plain en_US
dc.language.iso es en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Ingenierías es_ES
dc.title Real-time motion detection by lateral inhibition in accumulative computation. es_ES
dc.type info:eu-repo/semantics/article en_US

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