Shape Memorization and Recognition of 3D Objects Using a Similarity-Based Aspect-Graph Approach

This work proposes an incremental combinational algorithm to generate the prototype of a 3D object using 2D images randomly sampled from a viewing sphere. Similarity-based aspect-graph, which contains a set of aspects and prototypes for these aspects, is employed to represent the database of 3D objects. Furthermore, the proposed algorithm is based on low-level features and similarity measures between the features. In this work, the Fourier descriptor and point-to-point lengths are adopted as features, and three similarity measures, called the 1-norm, 2-norm, and K-L distance, are adopted to extract characteristic views. The effectiveness of the proposed algorithm is demonstrated by experiments with an updating mechanism.