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A novel set of moment invariants for pattern recognition applications based on jacobi polynomials

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2020) 12088 LNCS 139-148
DOI: 10.1007/978-3-030-49076-8_14
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Abstract

A novel set of moment invariants for pattern recognition applications, which are based on Jacobi polynomials, are presented. These moment invariants are constructed for digital images by means of a combination with geometric moments, and are invariant in the face of affine geometric transformations such as rotation, translation and scaling, on the image plane. This invariance is tested on a sample of the MPEG-7 CE-Shape-1 dataset. The results presented show that the low-order moment invariants indeed possess low variance between images that are affected by the mentioned geometric transformations.

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Rocha Angulo, R. A., Carpio, J. M., Rojas-Domínguez, A., Ornelas-Rodríguez, M., & Puga, H. (2020). A novel set of moment invariants for pattern recognition applications based on jacobi polynomials. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12088 LNCS, pp. 139–148). Springer. https://doi.org/10.1007/978-3-030-49076-8_14

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