Visual system based on artificial retina for motion detection.
We present a bioinspired model for detecting spatiotemporal features based on artificial retina response models. Event-driven processing is implemented using four kinds of cells encoding image contrast and temporal information. We have evaluated how the accuracy of motion processing depends on local contrast by using a multiscale and rank-order coding scheme to select the most important cues from retinal inputs. We have also developed some alternatives by integrating temporal feature results and obtained a new improved bioinspired matching algorithm with high stability, low error and low cost. Finally, we define a dynamic and versatile multimodal attention operator with which the system is driven to focus on different target features such as motion, colors, and textures.
Department of Computer Architecture and Technology, University of Granada, 18071 Granada, Spain. fbarranco@atc.ugr.es
Francisco
F
Barranco
Javier
J
Díaz
Eduardo
E
Ros
Begoña
B
del Pino
D000465:Algorithms; D001185:Artificial Intelligence; D003491:Cybernetics / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D009038:Motion; D012160:Retina / Q000502:physiology*; D013997:Time Factors