Yes I have. But what I found is that real vision is so complex, involving so many problems that must be solved and studied, that any attempt at general vision is beyond my current abilities. It would be like expecting a single person, such as myself, to figure out how to build the h-bomb all by themselves back before it had ever been done. It is the same scenario because it involves many engineering and scientific problems that must all be solved and studied.
You see in real vision you have a 3D world, camera optics, lighting issues, noise, blurring, rotation, distance, projection, reflection, shadows, occlusion, etc, etc, etc. It is many magnitudes more difficult than the problems I'm studying. Yet, really consider the two black squares problem. Its hard! It's so simple, yet so hard. I still haven't fully defined how to do it algorithmically... I will get to that in the coming weeks. So, to work on the full problem is practically impossible for me. Seeing as though there isn't a lot of support for AGI research such as this, I am much better served by proving the principle rather than implementing the full solution to the real problem. If I can even prove how vision works on simple black squares, I might be able to get help in my research... without a proof of concept, no one will help. If I can prove it on screenshots, even better. It would be a very significant achievement, if done in a truly general fashion (keeping in mind that truly general is not really possible). A great example of what happens when you work with real images is this... Look at the current solutions. They use features, such as sift. Using sift features, you might be able to say that an object exists with 70% certainty, or something like that. But, it won't be able to tell you what the object looks like, whats behind it. What is it occluding. What's next to it. What color is it. What pixels in the image belong to it. How are those parts attached. Etc. etc. etc. Now do you see why it makes little sense to tackle the full problem? Even the state of the art in computer vision sucks. It is great at certain narrow applications, but no where near where it needs to be for AGI. Dave On Mon, Jun 28, 2010 at 4:00 PM, Russell Wallace <russell.wall...@gmail.com>wrote: > On Mon, Jun 28, 2010 at 8:56 PM, David Jones <davidher...@gmail.com> > wrote: > > Having experience with the full problem is important, but forcing > yourself to solve every sub problem at once is not a better strategy at all. > > Certainly going back to a toy problem _after_ gaining some experience > with the full problem would have a much better chance of being a > viable strategy. Have you tried that with what you're doing, i.e. > having a go at writing a program to understand real video before going > back to black squares and screen shots to improve the fundamentals? > > > ------------------------------------------- > 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/?&; > Powered by Listbox: http://www.listbox.com > ------------------------------------------- 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
