Recognizing Planar Objects in 3-D Space

Object recognition is a major theme in computer vision. In this paper, we present a method of recognizing planar objects in 3-D space from a single image. Objects in a scene may be occluded, and the orientation of the objects is arbitrary. We represent each object by its dominant points, and pose the recognition problem as a dominant-point matching problem. We introduce a measure, known as sphericity, derived from an affine transform to indicate the quality of match among dominant points. A clustering algorithm, probe-and-block, is used to guide the matching. We use a least squares fit among dominant points to estimate object location in the scene. A heuristic measure is finally computed to verify the match.