The library now includes an interface to the excellent ChangChang Wu's GPU implementation of SIFT. Vision problems that were recently considered infeasible can now be easily solved in standard computers.
With a NVIDIA 9800 GT we obtain interest points and descriptors at ~ 25 fps for 640x480 images, depending on the level of detail in the scene. Fast GPU point matching is also provided.
(Using a 8600M GS we get ~10 fps. Most of the time is spent in the smallest scales, so we can get a big speedup if we start from octave 1.)
To appreciate the computing power of SiftGPU you should run the detector on a live camera. Here are some screenshots of a simple object recognition prototype. First, of course, the H&Z book:
Then Real World Haskell, detected just from a leg of the insect:
This works very well, but the number of known objects is small. We need a fast method for feature indexing.
SiftGPU requires specific hardware, but it is extremely useful. Clearly, easyVision must be finally cabalized so that we can install a basic, portable system, and any desired optional modules.