3D Object Recognition by Eigen-Scale-Space of Contours

People often recognize 3D objects by their boundary shape. Designing an algorithm for such a task is interesting and useful for retrieving objects from a shape database. In this paper, we present a fast 2-stage algorithm for recognizing 3D objects using a new feature space, built from curvature scale space images, as a shape representation that is scale, translation, rotation and reflection invariant. As well, the new shape representation removes the inherent ambiguity of the zero position of arc length for a scale space image. The 2-stage matching algorithm, conducted in the eigenspaces of the feature space, is analogous to the way people recognize an object: first identifying the type of object, and then determining the actual object. We test the new algorithm on a 3D database comprising 209 colour objects in 2926 different view poses, and achieve a 97% recognition rate for the object type and 95% for the object pose.

• Type: Conference
• Year: 2007
• Where: SCALESPACE
• Authors: Tim K. Lee, Mark S. Drew
• Keywords: Feature Space, Scale Space Image, SCALESPACE 2007, Shape Representation