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Generalized Multidimensional Orthogonal Polynomials with Applications to Shape Analysis
September 1990 (vol. 12 no. 9)
pp. 906-913

A technique using the generalized multidimensional orthogonal polynomials (GMDOP) for 2-D shape analysis is proposed. In shape analysis, spatial invariances (i.e. translational invariance, scaling invariance, rotational invariance, etc.) are important requirements for a shape analysis algorithm. The described technique provides not only the three invariant properties but also mirror-image rotational invariance and permutational invariance. Experimental results supporting the theory are presented.

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Index Terms:
2D images; picture processing; pattern recognition; multidimensional orthogonal polynomials; shape analysis; spatial invariances; translational invariance; scaling invariance; rotational invariance; permutational invariance; invariance; pattern recognition; picture processing; polynomials
Citation:
J. Xu, Y.H. Yang, "Generalized Multidimensional Orthogonal Polynomials with Applications to Shape Analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 9, pp. 906-913, Sept. 1990, doi:10.1109/34.57684
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