ComputerVisionWiki.org

http://ComputerVisionWiki.org

by pixcavator • 19 years ago

13 21 19 years ago

computervisionwiki.org

bluishgreen2• 19 years ago

Could any one suggest a good book on Vision. Something like Cormen for algorithms.

mnemonicsloth5• 19 years ago

Here are some background and pointers. I'm not a computer vision guy, but these might get you started.

Computer vision is really hard. The computational resources we can spend on the problem are orders of magnitude smaller than what we're trying to emulate in the nature. I think (IANACVS!) something like 35% of the human brain's processing capacity deals with vision, for example. So the discipline is both highly mathematical (to root out shortcuts and shrink the problem) and computationally intense. Also, it's often got some kind of real-time requirements.

Don't let me discourage you though. Since I don't know much about the field, I don't know any of its successes. CV researchers have produced useful systems.

1. Image transform methods: looking up the 2D Fourier transform might be a good start.

2. Variational methods. These are hard to explain as they relate to vision, but if you read the first few chapters of Sussman & Wisdom's Structure & Interpretation of Classical Mechanics, you'll get an idea of what they're talking about.

3. Anything that involves sensors also involves noise. At my school, the first stage of the graduate CV track is a course based on [Papoulis & Pillai]'s Probability, Random Variables, and Stochastic Processes.

4. One of my classmates was involved in object modeling with manifolds. A manifold is basically an n-dimensional shape that follows mostly-Euclidean rules like a sphere, torus, or pretzel. I have no idea what to recommend here, because my curiosity about manifolds and repeated frustration in my efforts to find out more about them ultimately landed me in grad school. Watch out for that.

5. Obviously I know more about software than hardware, but you could definitely do worse than reading up on commonly-used sensors. A midlevel text on CCD cameras would be the way to go here.

6. You can read up on Visual Perception in humans. When I took a class on this as an undergrad, it was a senior-level psych elective. A related field is Psychophysics, which uses methods from physics to study sensory stimuli (e.g. acoustic waves) as they relate to perceived experience. See #s 1 and 3 above.

Hope some of this helps.

angryprofessor• 19 years ago

For 4, try Calculus on manifolds, then Differential Geometry (both by Michael Spivak).

ecuzzillo3• 19 years ago

The reason there isn't a definitive text on vision is that vision doesn't work. Algorithms work, and so to have a definitive text, you just collect up all the notable ones that work, and write about them in a book. When you have a totally unsolved problem like vision (outside of certain highly constrained domains, like robot soccer, where the colors are guaranteed, or industrial tasks, where basically the whole scene is guaranteed), you basically have to choose which methods that currently don't work might have a chance of eventually leading to something that does work. There are many possible sets of choices for that, which is why you can't really have a definitive text.

DaniFong2• 19 years ago

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.

ivankirigin• 19 years ago

Saying that "vision doesn't work" is like saying "audio doesn't work". Outside a particular problem, you can't generalize to that level.

Automated surveillance from a fixed camera works extremely well (VSAM). Certain object class recognition like face or pedestrian detection works extremely well (Viola & Jones). Stereo depth extraction, some systems with amazing & cheap custom ASICs, works extremely well (Tyzx). 2D object instance recognition works extremely well (SIFT). This can power SLAM when combined with stereo imagery. Object Tracking works extremely well (Collins). Traversable road classification, car detection, road boundary & lane detection, and Stereo/SFM will power the first semi-autonomous cars. All that tech works really well today -- the task is system integration.

Also, vision isn't just visible light cameras. ASC is making a flash lidar that would make you drool. The "Swiss Ranger" is also good. Both can provide snap-shot volumetric 3D data combined with color and texture information, with no moving parts. They'll scale much better than scanning lidars and even multi-laser systems like Velodyne.

Add 3D processing like Spin Images to the mix, and integrated vision systems will get very, very powerful, very soon.

I like AnyBot's approach, but I think it is incorrect to assume "AI" won't be good enough and tele-operation will be king. If I were a pure-software robotics startup, I would focus on vision & navigation software, large/networked system integration/configuration/scalability utilities (like I'm sure you're building), and behavior systems.

For a text, I would recommend reading the winning papers from major vision/machine learning/robotics conferences, and the papers they reference. Grad students at CMU's Robotics Institute use Forsyth & Ponce's "computer vision, a modern approach". http://www.amazon.com/Computer-Vision-Approach-David-Forsyth...

I would also recommend Mitchell's "Machine Learning", Strang's "Linear Algebra and it's applications", and throw in Thrun's new "Probabilistic Robotics" for fun.

plinkplonk• 19 years ago

This (http://www.amazon.com/Introductory-Techniques-3-D-Computer-V...) is the best book on Computer Vision I 've come across. I first encountered it when trawling Stanford courses for good problem sets and research projects. Sebastian Thrun used to teach a class with this book (http://robots.stanford.edu/cs223b04/). It is fairly expensive and fairly mathematical but it is worth every cent you pay for it (imho).

Good Luck !

ed• 19 years ago

If anyone is considering building apps in this area, I'd strongly suggest checking out OpenCV.

It's _far_ more powerful than Pixcavator, well supported, liberally licensed, and provides some really awesome high-level API's.

pixcavator4• 19 years ago

If you know what 'the Laplacian of the Gaussian' is, definitely go for OpenCV. If you'd like to see something based on high school math, try this wiki.

ComputerVisionWiki.org

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