Computation of edge-edge-edge events based on conicoid theory for 3-D object recognition

The availability of a good viewpoint space partition is crucial in three dimensional (3-D) object recognition on the approach of aspect graph. There are two important events depicted by the aspect graph approach, edge-edge-edge (EEE) events and edge-vertex (EV) events. This paper presents an algorithm to compute EEE events by characteristic analysis based on conicoid theory, in contrast to current algorithms that focus too much on EV events and often overlook the importance of EEE events. Also, the paper provides a standard flowchart for the viewpoint space partitioning based on aspect graph theory that makes it suitable for perspective models. The partitioning result best demonstrates the algorithm's efficiency with more valuable viewpoints found with the help of EEE events, which can definitely help to achieve high recognition rate for 3-D object recognition.