While there have been countless important geometric and mathematical insights applied to/developed in computer vision in the last decade, it's really the rapidly increasing computational power available on commodity hardware that has made many advances in real time vision possible.
During the four years I was doing my PhD [in real time vision, a couple years ahead of Qi Pan in the same group], the amount of useful image processing and linear algebra/geometry that could be computed in 33 ms jumped dramatically.
The computer vision community is collectively realizing that techniques that were always mathematically and statistically sound, but considered too expensive for online processing, can now feasibly be incorporated into real time and interactive systems.
There are fewer rather than more 'tricks' now, and more straightforward representations of the underlying mathematical and probabilistic structures. This is a good thing.
Likewise, real-time reconstruction techniques appropriate for desktops in 2004 can now run very well on embedded platforms.
It's an exciting time for computer vision, as the set of feasible applications is exploding.
During the four years I was doing my PhD [in real time vision, a couple years ahead of Qi Pan in the same group], the amount of useful image processing and linear algebra/geometry that could be computed in 33 ms jumped dramatically.
The computer vision community is collectively realizing that techniques that were always mathematically and statistically sound, but considered too expensive for online processing, can now feasibly be incorporated into real time and interactive systems.
There are fewer rather than more 'tricks' now, and more straightforward representations of the underlying mathematical and probabilistic structures. This is a good thing.
Likewise, real-time reconstruction techniques appropriate for desktops in 2004 can now run very well on embedded platforms.
It's an exciting time for computer vision, as the set of feasible applications is exploding.