Title :
Silicon retina with correlation-based, velocity-tuned pixels
Author :
Delbrück, Tobias
Author_Institution :
California Inst. of Technol., Pasadena, CA, USA
fDate :
5/1/1993 12:00:00 AM
Abstract :
A functional two-dimensional silicon retina that computes a complete set of local direction-selective outputs is reported. The chip motion computation uses unidirectional delay lines as tuned filters for moving edges. Photoreceptors detect local changes in image intensity, and the outputs from these photoreceptors are coupled into the delay line, where they propagate with a particular speed in one direction. If the velocity of the moving edges matches that of the delay line, then the signal on the delay line is reinforced. The output of each pixel is the power in the delay line signal, computed within each pixel. This power computation provides the essential nonlinearity for velocity selectivity. The delay line architecture differs from the usual pairwise correlation models in that motion information is aggregated over an extended spatiotemporal range. As a result, the detectors are sensitive to motion over a wide range of spatial frequencies. The design of functional one- and two-dimensional silicon retinas with direction-selective, velocity-tuned pixels is described. It is shown that pixels with three hexagonal directions of motion selectivity are approximately (225 μm)2 in area in a 2-μm CMOS technology and consume less than 5 μW of power
Keywords :
CMOS integrated circuits; VLSI; analogue processing circuits; delay lines; motion estimation; neural chips; 2 micron; 5 muW; chip motion computation; delay line signal; hexagonal directions; image intensity; local direction-selective outputs; moving edges; photoreceptors; spatiotemporal range; tuned filters; two-dimensional silicon retina; unidirectional delay lines; velocity selectivity; velocity-tuned pixels; CMOS technology; Computer architecture; Delay lines; Filters; Image edge detection; Pairwise error probability; Photoreceptors; Retina; Silicon; Spatiotemporal phenomena;
Journal_Title :
Neural Networks, IEEE Transactions on