Aspect-based object recognition with size functions

An aspect-based system for the recognition of 3D objects from single view is presented. The system is based on the computation of size functions and consists of two stages: 1) models of the various aspects of the objects in a set are acquired from the corresponding edge maps, each model is represented by a feature vector and a training set is formed; and 2) a feature vector representing the shape of an object from a single previously unseen image is constructed and classified according to a k-nearest neighbour technique. The system was tested on a set of thirteen toy cars arbitrarily positioned on a turntable and viewed from a fixed, uncalibrated camera, and compared against methods based on moments (MB) and on Hausdorff distance (HDB). Since the system outperforms MB methods in terms of percentages of success and the HDB method in terms of efficiency, it is concluded that size functions can be very useful for aspect-based recognition