WHERE and WHAT: object perception for autonomous robots
Michael F. Kelly, Martin D. Levine · 2002
It is essential that autonomous robots be able to locate and identify objects in their environment. A novel approach for visually extracting such object information from images is presented. Annular operators are used to identify existing symmetric relationships between sets of edge elements. Operators are applied at multiple scales to edge data which have been extracted at multiple scales from a gray-scale image. From the resulting symmetry points, the authors identify a set of object parts in the scene. These are used as the basis for constructing coarse graph-based object descriptors. Preliminary results are presented to illustrate the approach using natural image data.