I am currently building a photographic setup which I intend to use to make reproductions of a large amount of transparencies(positive and negative film strips). As I will be using a fairly small (1") sensor and writing custom software for my rig, I intend to stitch together multiple images per frame. The way I intend to do this is by fitting the short (24mm) edge of the frame with the long(12mm) edge of my sensor and taking 3-4 overlapping images per frame. As all of this will be automated, the time consumption isn't really an issue.
The camera I am using has a ccd sensor of approximately 12x10mm and I will be using a lens from a film scanner (Minolta Dimage Scan 5400) as it is known to have high resolution and a very flat field.
never the less, in an effort to obtain the best possible results, I am currently looking into buying a distortion target to characterize the lens and minute errors in optical alignment at accquisition time and eventually undistort the images. This is because I would like to stitch the shots together and there for it is paramount to remove any distortion whatsoever.
While I have taken a course in Photogrammetric Computer Vision and am generally familiar with camera calibration, the fact that I am trying to characterize a static system with a perfectly planar image, is confusing me somewhat and I am uncertain what size/resolution distortion target would best suit my requirements.
I am tempted to think, that a couple frames 1-3(even one if the target is large enough) with a high resolution dot grid(such as this one ) would be enough and that I don't need 20+ images as one does in conventional camera calibration.
My question is: How many frames does one need to asses and correct distortion in a numerically stable way? Should the distortion target fill the entire frame or are multiple shots with shifts prefereable? Is there a specific term more suited to what I am trying to do than camera calibration, as I am trying to characterize the error in the whole system?
I will be using OpenCV.