DETECTION AND CORRECTION OF CHANGES IN EXTERIOR AND INTERIOR ORIENTATIONS WHILE ESTIMATING 3-D OBJECT DEFORMATIONS FROM MULTIPLE IMAGES WITH WEAK OR STRONG IMAGING GEOMETRY
Keywords: Photogrammetry, Close Range, Change Detection, Orientation, Reconstruction, Estimation, Parameters, Accuracy
Abstract. The paper deals with estimation of 3-D object deformation from multiple images initially in fixed positions with weak or strong imaging geometry. A new method is proposed to detect automatically if the exterior or interior orientations (rotations, translations, focal length, principal point) of one or several images have changed and which image or images contain the error, when the object deforms at the same time. The method is based on comparing novel feature vectors computed for each image from changes in the image coordinates of the object points and from residuals derived from the collinearity equations. Bundle adjustment is performed to simultaneously estimate the deformation of the object and to correct the changed orientations of the images. The rigidity needed in the weak case is obtained by approximating the deformation by a novel shape function containing parameters the values of which are estimated during adjustment. Test results with synthetic data show that even rather small changes in one orientation parameter of one image can be detected with high confidence. Weak imaging geometry allows to detect smaller changes than the strong one. The closer an initial approximation of deformation is available, the higher is the probability of correct detection. Subsequent correction of changed orientations and estimation of deformation may provide a high accuracy of 1:140000 of the object dimensions for both weak and strong imaging geometries, when the noise level in the image measurements is 0.1 pixel. Experiments with real data illustrate the good performance of the methods.