3 ms·

The human eye has much higher sensitivity range and resolution than any sensor I know of. The human eye does the equivalent of what you might call supersamplin

The human eye has much higher sensitivity range and resolution than any sensor I know of. The human eye does the equivalent of what you might call supersampling in computer terms. The eyes make very subtle focus shifts to change perception of the same object and get a more detailed picture of something that is difficult to see. No mechanical lens that I know of can move this fast or accurately but you can achieve something similar with multiple cameras. The brain also filters out extraneous data and interpolates missing data. Human vision is far from perfect though. Light changes that exceed you sensitivity range will blind you. Vision is impaired by darkness. The filtering and interpolation mechanisms of the brain are error prone and the brain often does not know when it is wrong. You may see something and never know it wasn't real, or don't see something and never know it was there.

radarsat1 9y ago
> The filtering and interpolation mechanisms of the brain are error prone and the brain often does not know when it is wrong. You may see something and never know it wasn't real, or don't see something and never know it was there. But it's hard to say necessarily that it's not optimal -- that is, that a digital version could necessarily do better. We are biased by our priors, just as a digital pattern matcher is biased by its training set. And in low light, we try to find patterns that we recognize in the noise, reconstruct the missing parts, etc., just as a computer would. A digital system might eventually have better resolution and be able to do better on some scale of precision and performance, but I suspect that most attempts to improve performance by introducing "supersampling" and pattern matching and other forms of inference will always result in similar errors to the brain. Perhaps different in character due to differences in the training set and algorithms, but of a similar nature.

radarsat1 9y ago

> The filtering and interpolation mechanisms of the brain are error prone and the brain often does not know when it is wrong. You may see something and never know it wasn't real, or don't see something and never know it was there. But it's hard to say necessarily that it's not optimal -- that is, that a digital version could necessarily do better. We are biased by our priors, just as a digital pattern matcher is biased by its training set. And in low light, we try to find patterns that we recognize in the noise, reconstruct the missing parts, etc., just as a computer would. A digital system might eventually have better resolution and be able to do better on some scale of precision and performance, but I suspect that most attempts to improve performance by introducing "supersampling" and pattern matching and other forms of inference will always result in similar errors to the brain. Perhaps different in character due to differences in the training set and algorithms, but of a similar nature.