Moment Invariants Of Perspective Transformation

Abstract

One of the fundamental problems in pattern recognition, computer vision, and scene analysis is the recognition of objects independent of size, translation, rotation, and perspective transformation. Perspective transformations are important because any lens system induces such a transformation. Extraction of attributes that are invariant under such transformations is important, because such attributes exploit the essential, nonchanging, and discriminatory natures of the physical objects Moreover, using such invariant attributes significantly reduces the complexities of the computation and the storage requirements of recognition systems. In this paper we derive a unique set of moment invariants of perspective transformation. We also provide experimental results for a set of geometrical 3D objects verifying the invariancies and uniqueness of the derived moments for several different & arbitrary perspective transformations.