Robust Identification of Locally Planar Objects Represented by 2D Point Clouds under Affine Distortions

Abstract: The matching of point sets that are characterized only by their geometric configuration is a challenging problem. In this paper, we present a novel point registration algorithm for robustly identifying objects represented by two dimensional point clouds under affine distor- tions. We make no assumptions about the initial orientation of the point clouds and only incorporate the geometric configuration of the points to recover the affine transformation that aligns the parts that originate from the same locally planar surface of the three dimensional object. Our algorithm can deal well with noise and outliers and is inherently robust against partial occlusions. It is in essence a GOODSAC approach based on geometric hashing to guess a good initial affine transformation that is iteratively refined in order to retrieve a characteristic common point set with minimal squared error. We successfully apply it for the biometric identification of the bluespotted ribbontail ray Taeniura lymma.