Optic flow is the perceived visual motion of objects as the observer moves relative to them. For example, say you are driving a car. A sign on the side of the road would move from the center of your vision to the side, growing as you approached. If you had 360 degree vision, this sign would proceed to move quickly past your side to your back, where it would shrink. This motion of the sign is its optic flow.
This allows you to judge how close you are to certain objects, and how quickly you are approaching them. It is also useful for avoiding obstacles: if an object in front of you is expanding but not moving, you are probably headed straight for it, but if it is expanding but moving slowly to the side, you will probably pass by it. Since optic flow relies only on relative motion, it remains the same when you are moving and the world remains still, and when you are standing still but everything you can see is moving past you. These properties have made the concept useful for robot designers writing visual navigation routines. It also appears to be used by certain insects, especially flying ones, where a large optic flow (indicating a quickly approaching obstacle) triggers muscles to move away.
Optical flow is a concept for considering the motion of objects within a visual representation. Typically the motion is represented as vectors originating or terminating at pixels in a digital image sequence
Optical flow is useful in pattern recognition, computer vision, and other image processing applications. It is closely related to motion estimation and motion compensation. Often the term optical flow is used to describe a dense motion field with vectors at each pixel, as opposed to motion estimation/compensation which uses vectors for blocks of pixels as in video compression methods such as MPEG.
Methods for determining optical flow Edit
- Phase correlation (inverse of normalized cross power spectrum)
- Block correlation (sum of absolute differences, normalized cross-correlation)
- Gradient Constraint based registration