Paper Info
Title
Lateral interaction in accumulative computation: a model for motion detection
Abstract
Some of the major computer vision techniques make use of neural nets. In this paper we present a novel model based on neural networks denominated lateral interaction in accumulative computation (LIAC). This model is based on a series of neuronal models in one layer, namely the local accumulative computation model, the double time scale model and the recurrent lateral interaction model. The LIAC model usefulness in the general task of motion detection may be appreciated by means of some significant examples of object detection in indefinite sequences of synthetic and real images.
Year
DOI
Venue
2003
10.1016/S0925-2312(02)00571-4
Neurocomputing
Keywords
Field
DocType
Accumulative computation,Lateral interaction,Double time scale,Motion detection,Image sequences
Scale model,Object detection,Computer vision,Motion detection,Pattern recognition,Computer science,Interaction model,Artificial intelligence,Artificial neural network,Machine learning,Computation
Journal
Volume
ISSN
Citations 
50
0925-2312
32
PageRank 
References 
Authors
1.49
16
4
Name
Order
Citations
PageRank
Antonio Fernández-Caballero11317117.99
Jose Mira2684.92
Ana E. Delgado García38710.85
Miguel Angel Fernández4768.20