It's a shame that Evolution Robotics weren't able to develop that system further. A logical progression would be to extend the geometric hashing to 3D and eventually 4D, although that would require a stereo camera or some other way of measuring distances to the observed features. Even so that demo program of theirs remains as one of the more impressive examples of invariant object recognition. You can present objects at all kinds of different rotations and scales and still have the program locate them, even within a noisy webcam image.
I know one guy who bought an Evolution robot some years ago. He took it to a primary school and demonstrated how it recognised different objects. He said that the robot could recognise the objects and speak their names faster than the children could.
On 23/10/06, Neil H. <[EMAIL PROTECTED]> wrote:
On 10/23/06, Bob Mottram <[EMAIL PROTECTED]> wrote:
>
> Another interesting development is the rise of the use of invariant feature
> detection algorithms together with geometric hashing for some kinds of
> object recognition. The most notable successes to date have been using
> David Lowe's SIFT method, which I think bears some resemblence to earlier
> methods such as Moravec's interest operator. To an extent these are just
> old algorithms developed in the 1980s enjoying a new lease of life within a
> more favourable computational environment.
Heh, I know what you mean. In the computer vision/recognition
literature, it almost seems like the non-deformable stuff can be split
into pre-SIFT and post-SIFT. After Lowe's SIFT paper came out in 1999,
it's kind of tricky to find a non-face visual recognition paper that
doesn't use SIFT or some derivative. More recently, representations
which take advantage of more contextual information (like Belongie's
Shape Context or Berg's geometric blur) seem rather interesting, but
I'm not sure how much they've proven themselves yet.
For those of you who haven't seen SIFT in action, there's a neat
downloadable demo available from Evolution Robotics:
http://www.evolution.com/core/ViPR/ (registration required)
It's been a few years since I've tried the demo myself, but if I
recall correctly, you just plug a webcam into your computer, and you
can use the program to learn to visually recognize different objects
you hold in front of it.
-- Neil
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