An evaluation of the novel ''normalized-redundancy'' representation for planar curves

In this paper, a novel approach for improving model-based recognition is proposed. Our approach provides a suitable shape representation by extracting only the most significant scales that best describe a planar noisy curve. The proposed representation satisfies several necessary criteria for general-purpose shape representation methods. The representation is capable of dealing with different levels of noise, it does not require user-set parameters or prior knowledge about the curve's nature, it also has a very low-order polynomial computational complexity in time and space. Hence such a shape representation is very useful for shape recognition. The method depends on the connection between the redundancy of two signals' smoothed versions and the essential structure being simultaneously isolated in both versions. Two different ways of formulating this approach are described in this paper: the global ''normalized-redundancy'' representation and the local ''normalized-redundancy'' representation. Results of applying the proposed formulation to synthetic and real 2-D shapes are presented.