Two-dimensional Object Recognition Based on the Method of Moving Frame

Invariant features play a key role in object and pattern recognition studies. Features that are invariant to geometrical transformations offer succinct representations of underlying objects so that they can be reliably identified. In this paper, a family of novel invariant features is introduced based on Cartan’s theory of moving frames. These new features is called summation invariants. Compared to existing invariant features, summation invariants are inherently numerically stable, and do not require computationally complex numerical integrations or analytical representations of underlying data. A robust methods for extracting summation invariants from sampled 2D contours introduced. Then, these new invariant features are applied to 2D object recognition and compared to other methods, e.g. wavelet and found to be more efficient.