The properties of viewed angles and distances with application to 3-D object recognition

Two novel probabilistic models for viewed angles and distances are derived by using a probability sphere method. The method is based on the assumption that the a priori probability density is isotropic for all viewing orientations of the scene. From these models two rules are suggested that deal with imaging of angles and distances. Rule A (B): there is high probability that the ratio of the scene angles (distances) to their imaged angles (distances) is closed to unity (the unique scale factor). These rules are applied to the recognition of 3-D objects which are represented by their linear features primitives. The parameters of the stochastic labeling algorithm, that is used for the recognition are estimated from angles and distances using both models. Various synthetic and real objects have been recognized by this approach