Graphics Reference
In-Depth Information
Fig. 1.7
The circuit and logic diagrams for a two-pixel imager. Through the cross-coupled feedback,
each pixel inhibits the other. It is essentially a flip-flop, with two known stable states, that can hang
for a while in an unstable all-dark state after being reset to that state (like an RS flip-flop with both
set and reset asserted at the same time). The gates on the right detect “Ready” when the “Reset”
signal has put the imager into the unstable state (Pixel-Light-1 and Pixel-Light-2 both low), and
“Done” when light discharging one or both photodiodes has allowed the flip-flop to commit to one
of the stable states (Pixel-Light-1 or Pixel-Light-2 high)
part of the strategy for wide dynamic range vision [
3
]: “If critical limiting factors are
emphasized one says that lateral inhibition, color opponency, and the gain changes
of light and dark adaptation, are necessary to transmit information about the light
intensities in subdivisions of the visual image, because the available information has
a much wider dynamic range than can be transmitted directly down a nerve fiber in
a reasonable time interval.” It may be that I had already inferred that from some of
his pre-1981 writings, though they were not as clear on this point.
My original chip layout, done “by hand” on a Xerox Alto with a mouse and the
ICARUS IC editor [
14
], is shown in Fig.
1.11
. I had no idea how much light would
be needed, or what the contrast ratio of the imaged surface would be, so I used lateral
inhibition to make the logical function independent of overall light level. I did no
calculations of photodiode capacitance, photon flux, noise margins, or any of the
things that I had to learn about decades later, designing image sensors at Foveon.
I just wanted to make sure it would work, and generate trackable binary patterns, at
any light level. It was very clear that a linearly responding sensor system would be
useless, but I saw the way to use the well-studied networks of lateral inhibition for
their nonlinear normalizing effects. This approach is nowmore common, in concepts
