Curve normalization for shape retrieval

In this paper, we propose a novel part-based approach for two dimensional (2-D) shape description and recognition. According to this method, first the polygonal approximation is employed to represent the outline shape by an ordered sequence of parts. Then using the Least squares model, each part is associated with a cubic polynomial curve. The obtained curves are normalized that are invariant to scaling, rotation and translation. Finally, based on shape similarity of resulting curves, a shape similarity between an input shape and its reference model is defined. A two-step matching algorithm is proposed. Experiments using several benchmark databases are performed and the obtained retrieval results demonstrate that the proposed approach is effective as compared to other matching techniques.

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