Homography decomposition

1. What is homography in image processing?
2. Why does homography have 8 degrees of freedom?
3. What is the homography matrix?
4. What is homography in OpenCV?

What is homography in image processing?

In the field of computer vision, any two images of the same planar surface in space are related by a homography (assuming a pinhole camera model). This has many practical applications, such as image rectification, image registration, or camera motion—rotation and translation—between two images.

Why does homography have 8 degrees of freedom?

Also, homography is defined upto a scale (c in above equation) i.e. it can be changed by a non zero constant without any affect on projective transformation. Thus, homography has 8 degree of freedom even though it contains 9 elements (3x3 matrix) i.e. the number of unknowns that need to be solved for is 8.

What is the homography matrix?

The homography matrix is a 3x3 matrix but with 8 DoF (degrees of freedom) as it is estimated up to a scale. It is generally normalized (see also 1) with h_33 = 1 or h_11^2 + h_12^2 + h_13^2 + h_21^2 + h_22^2 + h_23^2 + h_31^2 + h_32^2 + h_33^2 = 1 .

What is homography in OpenCV?

Homography is a transformation that maps the points in one point to the corresponding point in another image. The homography is a 3×3 matrix : If 2 points are not in the same plane then we have to use 2 homographs. Similarly, for n planes, we have to use n homographs.