Polyhedral Object Recognition by Indexing
This addresses the indexing problem in computer vision for recognizing objects in large databases, though it appears incremental as it builds on existing graph-based representations.
The paper tackles the problem of recognizing 3-D polyhedral objects from 2-D images using an indexing approach to avoid traditional matching, introducing a novel method based on polynomial characterization and hashing for graph indexing, with experimental results evaluating system performance.
In computer vision, the indexing problem is the problem of recognizing a few objects in a large database of objects while avoiding the help of the classical image-feature-to-object-feature matching paradigm. In this paper we address the problem of recognizing 3-D polyhedral objects from 2-D images by indexing. Both the objects to be recognized and the images are represented by weighted graphs. The indexing problem is therefore the problem of determining whether a graph extracted from the image is present or absent in a database of model graphs. We introduce a novel method for performing this graph indexing process which is based both on polynomial characterization of binary and weighted graphs and on hashing. We describe in detail this polynomial characterization and then we show how it can be used in the context of polyhedral object recognition. Next we describe a practical recognition-by-indexing system that includes the organization of the database, the representation of polyhedral objects in terms of 2-D characteristic views, the representation of this views in terms of weighted graphs, and the associated image processing. Finally, some experimental results allow the evaluation of the system performance.