Article ID Journal Published Year Pages File Type
530557 Pattern Recognition 2014 17 Pages PDF
Abstract

In this work we introduce a generalized expression of the weighted dual Hahn moment invariants up to any order and for any value of their parameters. In order for the proposed invariants to be formed, the weighted dual Hahn moments (up to any order and for any value of their parameters) are expressed as a linear combination of geometric ones. For this reason a formula expressing the n  th degree dual Hahn polynomial, for any value of its parameters, as a linear combination of monomials (cr·xrcr·xr), is proved. In addition, a recurrent relation for the fast computation of the aforementioned monomials coefficients (cr) is also given. Moreover, normalization aspects of the generalized weighted dual Hahn moment invariants are discussed, while a modification of them is proposed in order to avoid their numerical instabilities. Finally, experimental results and classification scenarios, including datasets of natural scenes, evaluate the proposed methodology.

► A generalized formula for computing the dual Hahn moment invariants. ► Dual Hahn moment invariants for orders greater than two. ► Dual Hahn moment invariants for Hahn parameters “a” and “c” other than zero. ► The proposed formula is practically free of overflow issues.

Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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