Matching images using a random sample consensus
When two cameras observe the same scene, they see the same elements but under different viewpoints. We have already studied the feature point matching problem in the previous chapter. In this recipe, we come back to this problem, and we will learn how to exploit the epipolar constraint between two views to match image features more reliably.
The principle that we will follow is simple: when we match feature points between two images, we only accept those matches that fall on the corresponding epipolar lines. However, to be able to check this condition, the fundamental matrix must be known, but we need good matches to estimate this matrix. This seems to be a chicken-and-egg problem. However, in this recipe, we propose a solution in which the fundamental matrix and a set of good matches will be jointly computed.
How to do it...
The objective is to be able to compute a fundamental matrix and a set of good matches between two views. To do so, all...
