Image rectification is a transformation process used to project two-or-more images onto a common image plane. It corrects image distortion by transforming the image into a standard coordinate system.
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Stereo vision uses triangulation based on epipolar geometry to determine distance to an object.
Between two cameras there is a problem of finding a corresponding point viewed by one camera in the image of the other camera (known as the correspondence problem). In most camera configurations, finding correspondences requires a search in two-dimensions. However, if the two cameras are aligned to be coplanar, the search is simplified to one dimension - a horizontal line parallel to the baseline between the cameras. Furthermore, if the location of a point in the left image is known, it can be searched for in the right image by searching left of this location along the line, and vice versa (see binocular disparity). Image rectification is an equivalent (and more often used[1]) alternative to perfect camera alignment. Image rectification is usually performed regardless of camera precision due to
If the images to be rectified are taken from camera pairs without geometric distortion, this calculation can easily be made with a linear transformation. X & Y rotation puts the images on the same plane, scaling makes the image frames be the same size and Z rotation & skew adjustments make the image pixel rows directly line up. The rigid alignment of the cameras needs to be known (by calibration) and the calibration coefficients are used by the transform[2].
In performing the transform, if the cameras themselves are calibrated for internal parameters, an essential matrix provides the relationship between the cameras. The more general case (without camera calibration) is represented by the fundamental matrix. If the fundamental matrix is not known, it is necessary to find preliminary point correspondences between stereo images to facilitate its extraction[2].
Stereo images can also be taken with a single camera in motion. In this case the relationship of the images can have significant forward-motion components, and a linear transformation may produce severely warped images or very large images. Non-linear transformation techniques can be used to manage this difficulty[3][1][4].
There are basically three algorithms for image rectification: planar rectification [5], cylindrical rectification[1] and polar rectification[3][4][6].
Image rectification in GIS converts images to a standard map coordinate system. This is done by matching ground control points (GCP) in the mapping system to points in the image. These GCPs calculate necessary image transforms[7].
Primary difficulties in the process occur
The maps that are used with rectified images are non-topographical. However, the images to be used may contain distortion from terrain. Image orthorectification additionally removes these effects[7].
Image rectification is a standard feature available with commercial GIS software packages.