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 ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/[EMAIL PROTECTED]
