I'm learning about using image registration to align two images, e.g., two photographs of the same scene taken from a slightly different location. What factors influence how well we can align the images? Does the distance to the objects in the image influence its effectiveness of alignment, and if so how? What about the amount of variation in the distances to the objects in the images: how does that influence the effectiveness of the alignment?
Background: I came across the notion of "global image registration" when reading the paper
"Mutual information based registration of multimodal stereo videos for person tracking", by Stephen J Krotosky and Mohan M Trivedi, in Computer Vision and Image Understanding 2007.
I believe "global image registration" means to use one transformation function for the registration. The authors state that global image registration is accurate when all objects of interest are in the same plane. I am trying to figure out exactly what they mean.
At first, I assumed that the authors mean objects across different frames (then needed to have a similar distance to the camera across the frames), where the previously calculated registration is supposed to work for each frame.
Then I thought that they maybe mean the registration of a single image pair and that objects of interest must be in similar distance to the camera.
To check this, I registered two pairs of image. The first pair has a lot of depth, the second pair does not, i.e., the first pair contains objects of varying distances to the cameras. It can be seen that the latter is much better registered than the former. This suggests that my second statement is correct: an image registration based on one transformation function is not able to register an image well if there is a lot of variation in the distances to the objects in the image. Is this correct?