bundle block adjustment with 3d natural cubic splines

⎡⎢⎣x i − x py i − y p−f⎤⎡⎥⎦ = λR T ⎢(ω, φ, κ) ⎣X i − X oY i − Y oZ i − Z o⎤⎥⎦ (2.8)where λ scale, x i , y i the image coordinates, X i , Y i , Z i the object coordinates, x p , y p , finterior orientation parameters and X o , Y o , Z o , ω, φ, κ exterior orientation parameters.R T is the 3D orthogonal rotation matrix containing the three rotation angles ω, φ andκ.Linear features between image and object space were compared to calculate EOPs bythe modified iterated Hough transform. In the result, EOPs were robustly estimatedwithout the knowledge of the relationship between image and object space primitives.In addition, Habib et al.[28] demonstrated that straight lines in object spacecontributed for performing the bundle block adjustment with self-calibration. Theyproposed that a large number of GCPs were reduced in calibration because of linearfeatures having four collinearity equations from two end points defining each straightline. Their experiment proposed the usefulness of linear features in orientation notonly with frame aerial imagery, but also in bundle block adjustment.Habib et al.[25] summarized linear features extracted from mobile mapping system,GIS database and maps for various photogrammetric applications such as single photoresection, triangulation, digital camera calibration, image matching, 3D reconstruction,image to image registration and surface to surface registration. Matched linearfeature primitives are utilized in space intersection for reconstruction of object spacefeatures and linear features in the object space are used for control features in triangulationand digital camera calibration. Since linear feature extraction can meetsub-pixel accuracy across the direction of edge and linear features can be extracted17