Comment by DaniFong | Hacker News Reader

Comment by DaniFong | Hacker News Reader

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What do you mean 'work'? There are many highly nontrivial successes in computer vision. Take a look at some of the examples here: http://yann.lecun.com/exdb/lenet/index.html

They're pretty surprisingly effective. Or how about melanoma detection and discrimination from benign lesions here: http://scholarsmine.umr.edu/post_prints/00312091_09007dcc803...

Or with this thing? http://www.polartechnics.com.au/IRM/Content/products_solar_o...

Mathematics was hardly cut and dry before Bertrand Russell wrote Principia Mathematica, but that book still have tremendous value (if only as a beacon!) There was plenty more to discover in geometry when Euclid wrote the elements.

A good computer vision book, even one that simply cataloged important successes and failures, would be quite valuable. It's not that it hasn't been written because it can't be, it's that it hasn't been written because no one has really gotten around to it. Good textbooks require a massive amount of effort to get right.

It doesn't work in the sense everybody most wants it to work, which is to slap a cheap CCD camera or three on top of a mobile robot and have it tell the robot everything about its surroundings, like human vision can. Yeah, if you point a camera at a very restricted specific set of objects, all lit the same way, and all in generally the same basic configuration, you can do some vaguely useful statistics to get some information out of it, but that's kind of a painfully limiting thing.

Put it this way: At Anybots, we have a robot with 16 cameras on its head. We pump all those cameras back to an operator station, stitch them all together in a vaguely fitting-together way, and then project them onto a big bank of monitors. Plop a human operator in front of the monitors, and he knows exactly the orientation of the robot, the configuration of its limbs, and has basically a 3d map of the entire scene and a model of the lighting, minus occlusions. I'm pretty sure nobody knows how to write a computer vision system to do that. I'm not even sure that anybody knows how to use the overlap in the cameras to figure out how to make a coherent nice projection of the cameras onto the monitors.

Sure, it doesn't work like magic. There's still a decent case for a book...

Computer vision is classified under AI in wikipedia. That pretty much sums up what we are trying to arrive at here.

It's interesting -- I started writing this exact definition of "works" in my post above. Anthropomorphic bias, right?

Even with this understanding, though, I can't wrap my brain around what you're saying above. Computer vision doesn't "work" (for our definition of "work") because it's not feasible to get enough processing power together:

O( 3e10 ) neurons * 100 Hz/neuron = more cycles than you can rent on an NSF grant.

We've definitely got some exponential growing to do before we can even start meaningful experimentation. Does that mean no work should be done in the meantime?

Yes, it is a bias. I think it is a big mistake to try to solve the problem by emulating how humans see because we don't know how humans see (here's a rant of mine on a related topic http://inperc.com/blog2/2007/10/12/%e2%80%9cbrain-inspired%e...).

I don't think we're doing that much processing to do vision. It's never the case that even close to all the cells in the visual cortex are being used. It's always a tiny fraction of that. Yeah, we have a bunch of cells that neuroscientists think are somehow related to doing vision, but there's no evidence that they're doing some kind of horribly computationally intensive thing all day long. If they were, we'd see it on MRI's as soon as we put a picture in front of somebody.

No, I think we just haven't figured out what to do, not that we're limited by computational resources. I think there are a bunch of pretty strong assumptions hardwired into the brain, so we can see physical objects, but then you can fool us by showing us images that mess with the assumptions (aka optical illusions).

(Not that this is a particularly useful idea, mind you. I'm not about to solve computer vision. It's just a random philosophical idea.)

It's never the case that even close to all the cells in the visual cortex are being used. It's always a tiny fraction of that.

This is also true of any individual transistor in a processor. An ALU contains hardware to perform many operations, but at most performs one per cycle. You can increase utilization through parallelism and pipelining, but you pay for it with more synchronization and control hardware that only switches on to resolve conflicts. And nowadays, half of all the transistors in a CPU are part of a cache (every cycle, you need a word or two, but get the whole block). You can't conclude that they're not making a contribution to output from the fact that they're often inactive.

No, I think we just haven't figured out what to do, not that we're limited by computational resources.

Based on what evidence? We know that the brain is hugely complex. We know it can sort through hugely complex problem spaces at least some of the time. Doesn't it seem like wishful thinking to assume both that the brain is really inefficient and that most of the problems it solves actually have simple hueristic solutions?

I'll step up and take ownership of the end result of this argument: If what you say is true and understanding is all we lack, why have these problems proved so stubborn?

>If what you say is true and understanding is all we lack, why have these problems proved so stubborn?

Understanding is hard, why is this so surprising?

Because understanding is almost always easier than data collection and tool-building.

Pure lack of understanding -- having all the data and having no idea what it means -- can keep a problem open for a generation or two. Einstein is famous because he solved a problem in EM physics that was at most 30 years old.

Lack of data or meaningful investigative techniques can keep a subject crippled for centuries. Physics went from rolling marbles to detecting radio waves in the two hundred fifty years since Galileo, while biology was pretty much stagnant. Without organic chemistry, even scientists were willing to believe that living organisms were made of some special God-stuff. Contrast that to the last 20 years. The human genome has been sequenced by biologists on their way to designing new organisms while physicists have been stuck throwing models around since the 1970s. Petri dishes are cheaper than supercolliders.

Now let's look at AI and machine vision. Our primary tools, brain imaging and computer simulation, are getting better every year. Maybe new ideas in algorithms or parallel architecture will bring "real" computer vision closer. In fact, they probably will. But even if they don't, this approach will definitely succeed eventually:

http://news.bbc.co.uk/2/hi/technology/6600965.stm

I don't have as many examples but here is one. Galileo discovered his principle by conducting very simple experiments with no special tools or a lot of data collection. How do you find the relevant data or build relevant tools if you don't have a clue about what you are supposed to discover?

What computer vision needs is mathematics, in my opinion. And math has nothing to do with "data collection and tool-building". It is entirely about understanding.

The link provides a good example of how people try to solve problems without any understanding. They don't know how the brain operates, yet they build its "model". And then they expect this model to solve the problem for them...

How can you say Galileo didn't use tools? He invented the telescope fer God's sake. He timed the descent of falling objects by hiring musicians to keep a beat (no watches). When he couldn't time things, he built ramps and arches to compare things side by side. Much of his machinery was didactic because he often needed to demonstrate his ideas to the courtiers who supported him. We credit him with the invention of the experimental method today because described them in language reminiscent of geometric proofs.

I agree with you about the CV thing, though. We should talk sometime...

Yeah, Galileo is a particularly bad example. I believe he had a professional instrument maker working for him full-time. Newton would illustrate the point better.

Galileo didn't invent the telescope, though. What he did do first is use telescopes for astronomy.

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