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A multiscale algorithm for matching and classifying 2-D shapes is developed. The algorithm uses the 1-D dyadic wavelet transform (DWT) to decompose a shape's boundary into multiscale levels. Then the coarse to fine matching and classification are achieved in two stages. In the first stage, the global features are extracted by calculating the curve moment invariants of the approximation coefficients. By calculating the normalized cross correlation of the 1-D triangle area representation of the detail coefficients, the local similarity is achieved by the second stage. The proposed algorithm is invariant to the affine transformation and to the boundary starting point variation. In addition, the results demonstrate that the new algorithm is not sensitive to small boundary deformations.
Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Volume:2 )
Date of Conference: 23-26 Aug. 2004