Shape retrieval using triangle-area representation and dynamic space warping
Pattern Recognition
Affine invariant matching of broken boundaries based on particle swarm optimization
Image and Vision Computing
Pattern Recognition Letters
Journal of Signal Processing Systems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Coarse to fine K nearest neighbor classifier
Pattern Recognition Letters
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In this paper, 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.