bundle_adjust_mosaic — Perform a bundle adjustment of an image mosaic.
bundle_adjust_mosaic performs a bundle adjustment of an image mosaic. This can be used to determine the geometry of a mosaic as robustly as possible, and hence to determine the transformations of the images in the mosaic more accurately than with single image pairs.
To achieve this, the projective transformation for each overlapping image pair in the mosaic should be determined with proj_match_points_ransac. For example, for a 2×2 block of images in the following layout
the following projective transformations should be determined, assuming that all images overlap each other: 1->2, 1->3, 1->4, 2->3, 2->4 and 3->4. The indices of the images that determine the respective transformation are given by MappingSource and MappingDest. The indices are start at 1. Consequently, in the above example MappingSource = [1,1,1,2,2,3] and MappingDest = [2,3,4,3,4,4] must be used. The number of images in the mosaic is given by NumImages. It is used to check whether each image can be reached by a chain of transformations. The index of the reference image is given by ReferenceImage. On output, this image has the identity matrix as its transformation matrix.
The 3×3 projective transformation matrices that correspond to the image pairs are passed in HomMatrices2D. Additionally, the coordinates of the matched point pairs in the image pairs must be passed in Rows1, Cols1, Rows2, and Cols2. They can be determined from the output of proj_match_points_ransac with tuple_select or with the HDevelop function subset. To enable bundle_adjust_mosaic to determine which point pair belongs to which image pair, NumCorrespondences must contain the number of found point matches for each image pair.
The parameter Transformation determines the class of transformations that is used in the bundle adjustment to transform the image points. This can be used to restrict the allowable transformations. For Transformation = 'projective', projective transformations are used (see vector_to_proj_hom_mat2d). For Transformation = 'affine', affine transformations are used (see vector_to_hom_mat2d), for Transformation = 'similarity', similarity transformations (see vector_to_similarity), and for Transformation = 'rigid' rigid transformations (see vector_to_rigid).
The resulting bundle-adjusted transformations are returned as an array of 3×3 projective transformation matrices in MosaicMatrices2D. In addition, the points reconstructed by the bundle adjustment are returned in (Rows, Cols). The average projection error of the reconstructed points is returned in Error. This can be used to check whether the optimization has converged to useful values.
Number of different images that are used for the calibration.
Restriction: NumImages >= 2
Index of the reference image.
Indices of the source images of the transformations.
Indices of the target images of the transformations.
Array of 3×3 projective transformation matrices.
Row coordinates of corresponding points in the respective source images.
Column coordinates of corresponding points in the respective source images.
Row coordinates of corresponding points in the respective destination images.
Column coordinates of corresponding points in the respective destination images.
Number of point correspondences in the respective image pair.
Transformation class to be used.
Default value: 'projective'
List of values: 'affine', 'projective', 'rigid', 'similarity'
Array of 3×3 projective transformation matrices that determine the position of the images in the mosaic.
Row coordinates of the points reconstructed by the bundle adjustment.
Column coordinates of the points reconstructed by the bundle adjustment.
Average error per reconstructed point.
* Assume that Images contains the four images of the mosaic in the * layout given in the above description. Then the following example * computes the bundle-adjusted transformation matrices. From := [1,1,1,2,2,3] To := [2,3,4,3,4,4] HomMatrices2D :=  Rows1 :=  Cols1 :=  Rows2 :=  Cols2 :=  NumMatches :=  for J := 0 to |From|-1 by 1 select_obj (Images, ImageF, From[J]) select_obj (Images, ImageT, To[J]) points_foerstner (ImageF, 1, 2, 3, 100, 0.1, 'gauss', 'true', \ RowsF, ColsF, _, _, _, _, _, _, _, _) points_foerstner (ImageT, 1, 2, 3, 100, 0.1, 'gauss', 'true', \ RowsT, ColsT, _, _, _, _, _, _, _, _) proj_match_points_ransac (ImageF, ImageT, RowsF, ColsF, RowsT, ColsT, \ 'ncc', 10, 0, 0, 480, 640, 0, 0.5, \ 'gold_standard', 2, 42, HomMat2D, \ Points1, Points2) HomMatrices2D := [HomMatrices2D,HomMat2D] Rows1 := [Rows1,subset(RowsF,Points1)] Cols1 := [Cols1,subset(ColsF,Points1)] Rows2 := [Rows2,subset(RowsT,Points2)] Cols2 := [Cols2,subset(ColsT,Points2)] NumMatches := [NumMatches,|Points1|] endfor bundle_adjust_mosaic (4, 1, From, To, HomMatrices2D, Rows1, Cols1, \ Rows2, Cols2, NumMatches, 'rigid', MosaicMatrices, \ Rows, Columns, Error) gen_bundle_adjusted_mosaic (Images, MosaicImage, HomMatrices2D, \ 'default', 'false', TransMat2D)
If the parameters are valid, the operator bundle_adjust_mosaic returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.