Paper Info
Title
The Underlying Formal Model of Algorithmic Lateral Inhibition in Motion Detection
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.
Year
DOI
Venue
2007
10.1007/978-3-540-73055-2_14
IWINAC (2)
Keywords
Field
DocType
algorithmic lateral inhibition,finite state machine,computational model,artificial neural network,formal model,camera processor,motion detection,underlying formal model,automata modeling,motion detection task,neurally-inspired algorithmic lateral inhibition,recurrent neural network,computer model,lateral inhibition
Motion detection,Computer science,Automaton,Field-programmable gate array,Recurrent neural network,Finite-state machine,Lateral inhibition,Artificial intelligence,Artificial neural network,Machine learning
Conference
Volume
ISSN
Citations 
4528
0302-9743
0
PageRank 
References 
Authors
0.34
16
5
Name
Order
Citations
PageRank
José Mira120512.90
Ana E. Delgado224316.85
Antonio Fernández-Caballero31317117.99
María T. López432128.80
Miguel A. Fernández538031.84