Your computer monitor flashes 75 frames per second, but you don't notice any flicker because light sensing neurons have a response delay of about 100 ms. Motion detection begins in the retina by cells that respond to contrast between light and dark moving in specific directions computed by simple, fixed weight circuits. Higher up in the processing chain, you detect motion when your eyes and head smoothly track moving objects using kinesthetic feedback from your eye and neck muscles and input from your built in accelerometer in the semicircular canals in your ears. This is all very complicated of course. You are more likely to detect motion in objects that you recognize and expect to move, like people, animals, cars, etc.
-- Matt Mahoney, [email protected] ________________________________ From: David Jones <[email protected]> To: agi <[email protected]> Sent: Mon, June 21, 2010 9:39:30 AM Subject: [agi] Re: High Frame Rates Reduce Uncertainty Ignoring Steve because we are simply going to have to agree to disagree... And I don't see enough value in trying to understand his paper. I said the math was overly complex, but what I really meant is that the approach is overly complex and so filled with research specific jargon, I don't care to try understand it. It is overly converned with copying the way that the brain does things. I don't care how the brain does it. I care about why the brain does it. Its the same as the analogy of giving a man a fish or teaching him to fish. You may figure out how the brain works, but it does you little good if you don't understand why it works that way. You would have to create a synthetic brain to take advantage of the knowledge, which is not a approach to AGI for many reasons. There are a million other ways, even better ways, to do it than the way the brain does it. Just because the brain accidentally found 1 way out of a million to do it doesn't make it the right way for us to develop AGI. So, moving on.... I can't find references online, but I've read that the Air Force studied the ability of the human eye to identify aircraft in images that were flashed on a screen at 1/220th of a second. So, clearly, the human eye can at least distinguish 220 fps if it operated that way. Of course, it may not operate on fps second, but that is besides the point. I've also heard other people say that a study has shown that the human eye takes 1000 exposures per second. They had no references though, so it is hearsay. The point was that the brain takes advantage of the fact that with such a high exposure rate, the changes between each image are very small if the objects are moving. This allows it to distinguish movement and visual changes with extremely low uncertainty. If it detects that the changes required to match two parts of an image are too high or the distance between matches is too far, it can reject a match. This allows it to distinguish only very low uncertainty changes and reject changes that have high uncertainty. I think this is a very significant discovery regarding how the brain is able to learn in such an ambiguous world with so many variables that are difficult to disambiguate, interpret and understand. Dave On Fri, Jun 18, 2010 at 2:19 PM, David Jones <[email protected]> wrote: >I just came up with an awesome idea. I just realized that the brain takes >advantage of high frame rates to reduce uncertainty when it is estimating >motion. The slower the frame rate, the more uncertainty there is because >objects may have traveled too far between images to match with high certainty >using simple techniques. > >So, this made me think, what if the secret to the brain's ability to learn >generally stems from this high frame rate trick. What if we made a system that >could process even high frame rates than the brain can. By doing this you can >reduce the uncertainty of matches very very low (well in my theory so far). If >you can do that, then you can learn about the objects in a video, how they >move together or separately with very high certainty. > >You see, matching is the main barrier when learning about objects. But with a >very high frame rate, we can use a fast algorithm and could potentially reduce >the uncertainty to almost nothing. Once we learn about objects, matching gets >easier because now we have training data and experience to take advantage of. > >In addition, you can also gain knowledge about lighting, color variation, >noise, etc. With that knowledge, you can then automatically create a model of >the object with extremely high confidence. You will also be able to determine >the effects of light and noise on the object's appearance, which will help >match the object invariantly in the future. It allows you to determine what is >expected and unexpected for the object's appearance with much higher >confidence. > >Pretty cool idea huh? > >Dave > agi | Archives | Modify Your Subscription ------------------------------------------- agi Archives: https://www.listbox.com/member/archive/303/=now RSS Feed: https://www.listbox.com/member/archive/rss/303/ Modify Your Subscription: https://www.listbox.com/member/?member_id=8660244&id_secret=8660244-6e7fb59c Powered by Listbox: http://www.listbox.com
