Fuzzy quaternion approach to object recognition incorporating Zernike moment invariants

Abstract

A novel approach to 3-D object recognition based on fuzzy subset theory is described. This method uses Zernike moment invariants of the silhouette of the unknown object to form a set of fuzzy-weighted quantities called fuzzy quaternions. These are matched against those of known objects at predetermined viewpoints. The determination of the Zernike moment invariants can be faster if the equivalent contour integrals are calculated instead. By employing a novel ρ-correction scheme, errors due to the digitization are reduced. To speed up the recognition process, a modified Nelder-Mead simplex method is used. Preliminary results demonstrate the potential of the fuzzy quaternion as a viable basis for discrimination. It is concluded that the primary merits of this approach are the ease of model formation, the simplicity of the recognition scheme, and the speed of object recognition. Its disadvantages include the inability to recognize occluded objects and a poor object recognition rate for high perspective distortion of objects.