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<p>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.</p>
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

J. Xu and Y. Yang, "Generalized Multidimensional Orthogonal Polynomials with Applications to Shape Analysis," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 12, no. , pp. 906-913, 1990.
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