Name
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 8x8 LIAC module, has been tested on several video sequences, providing promising performance results. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.engappai.2009.08.006 | Eng. Appl. of AI |
Keywords | Field | DocType |
liac module,finite state machine,lateral inhibition,accumulative computation,motion detection task,general liac method,motion detection,recurrent neural network,real-time motion detection,computational model,formal model,artificial neural network,computer model,finite state automata,real time | Motion detection,Computer science,Recurrent neural network,Lateral inhibition,Finite-state machine,Artificial intelligence,Artificial neural network,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
23 | 1 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
10 | 0.57 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana E. Delgado | 1 | 243 | 16.85 |
| María T. López | 2 | 321 | 28.80 |
| Antonio Fernández-Caballero | 3 | 1317 | 117.99 |