with FabioCrosilla and Eleonora Maset
This stream of research focuses on applications of Procrustean analysis to computer vision and photogrammetry problems. Procrustes analysis is a well known least squares technique used to directly perform transformations among corresponding point coordinates belonging to a generic k-dimensional space. Our research aims to develop new analytical tools based on Procrustean methods for solving classical Computer Vision and photogrammetric problems. In [1] we derived the solution of the Anisotropic Extended Orthogonal Procrustes Analysis (AEOPA) with row-scaling and applied it to perform the exterior orientation of one image. We also provided [2] a robust version of this algorithm based on Forward Search, which proved to be highly effective and accurate in detecting outliers, even for small data size or high outliers contamination. In [3] we formulated the point-line registration problem, which generalizes absolute orientation to point-line matching, as an instance of the AEOPA model and derived its solution. The same formulation solves the Non-Perspective-n-Point camera pose problem, that in turn generalizes exterior orientation to non-central cameras, i.e., generalized cameras where projection rays do not meet in a single point. A generalized version of AEOPA leads instead to the Procrustean solution of the classical bundle block adjustment, developed in [4]. Moreover, we introduced [5] a robust variant of the algorithm based on Iteratively Reweighted Least Squares (IRLS), that achieves reliable results also in the presence of a percentage up to 10% of outliers.