A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Rocha Angulo, Rafael Augusto, Carpio, Juan Martín, Rojas-Domínguez, Alfonso, Ornelas-Rodríguez, Manuel, Puga, Héctor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297594/
http://dx.doi.org/10.1007/978-3-030-49076-8_14
_version_ 1783547039457476608
author Rocha Angulo, Rafael Augusto
Carpio, Juan Martín
Rojas-Domínguez, Alfonso
Ornelas-Rodríguez, Manuel
Puga, Héctor
author_facet Rocha Angulo, Rafael Augusto
Carpio, Juan Martín
Rojas-Domínguez, Alfonso
Ornelas-Rodríguez, Manuel
Puga, Héctor
author_sort Rocha Angulo, Rafael Augusto
collection PubMed
description 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.
format Online
Article
Text
id pubmed-7297594
institution National Center for Biotechnology Information
language English
publishDate 2020
record_format MEDLINE/PubMed
spelling pubmed-72975942020-06-17 A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials Rocha Angulo, Rafael Augusto Carpio, Juan Martín Rojas-Domínguez, Alfonso Ornelas-Rodríguez, Manuel Puga, Héctor Pattern Recognition Article 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. 2020-04-29 /pmc/articles/PMC7297594/ http://dx.doi.org/10.1007/978-3-030-49076-8_14 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Rocha Angulo, Rafael Augusto
Carpio, Juan Martín
Rojas-Domínguez, Alfonso
Ornelas-Rodríguez, Manuel
Puga, Héctor
A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials
title A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials
title_full A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials
title_fullStr A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials
title_full_unstemmed A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials
title_short A Novel Set of Moment Invariants for Pattern Recognition Applications Based on Jacobi Polynomials
title_sort novel set of moment invariants for pattern recognition applications based on jacobi polynomials
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297594/
http://dx.doi.org/10.1007/978-3-030-49076-8_14
work_keys_str_mv AT rochaangulorafaelaugusto anovelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT carpiojuanmartin anovelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT rojasdominguezalfonso anovelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT ornelasrodriguezmanuel anovelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT pugahector anovelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT rochaangulorafaelaugusto novelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT carpiojuanmartin novelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT rojasdominguezalfonso novelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT ornelasrodriguezmanuel novelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials
AT pugahector novelsetofmomentinvariantsforpatternrecognitionapplicationsbasedonjacobipolynomials

Ejemplares similares