The Underlying Formal Model of Algorithmic Lateral Inhibition in Motion Detection

Authors:
José Mira;Ana E. Delgado;Antonio Fernández-Caballero;María T. López;Miguel A. Fernández
Affiliations:
Universidad Nacional de Educación a Distancia, E.T.S.I. Informática, 28040 - Madrid, Spain;Universidad Nacional de Educación a Distancia, E.T.S.I. Informática, 28040 - Madrid, Spain;Universidad de Castilla-La Mancha, Instituto de Investigación en Informática (I3A) and, Escuela Politécnica Superior de Albacete, 02071 - Albacete, Spain;Universidad de Castilla-La Mancha, Instituto de Investigación en Informática (I3A) and, Escuela Politécnica Superior de Albacete, 02071 - Albacete, Spain;Universidad de Castilla-La Mancha, Instituto de Investigación en Informática (I3A) and, Escuela Politécnica Superior de Albacete, 02071 - Albacete, Spain
Venue:
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Year:
2007

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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.