  Computing the epipolar geometry from feature points between cameras with very
different viewpoints is often error prone, as an object's appearance can vary
greatly between images. For such cases, it has been shown that using motion
extracted from video can achieve much better results than using a static image.
This paper extends these earlier works based on the scene dynamics. In this
paper we propose a new method to compute the epipolar geometry from a video
stream, by exploiting the following observation: For a pixel p in Image A, all
pixels corresponding to p in Image B are on the same epipolar line.
Equivalently, the image of the line going through camera A's center and p is an
epipolar line in B. Therefore, when cameras A and B are synchronized, the
momentary images of two objects projecting to the same pixel, p, in camera A at
times t1 and t2, lie on an epipolar line in camera B. Based on this observation
we achieve fast and precise computation of epipolar lines. Calibrating cameras
based on our method of finding epipolar lines is much faster and more robust
than previous methods.
