Nonlinear correlation by using invariant identity vectors signatures to identify plankton

In this paper a new methodology to recognize radk>larians is presented. This system is invariant to position, rotation and scale by using identity vectors signatures (Is) obtained for both the target and the problem image. In this application, / is obtained by means of a simplification of the mai...

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Autores principales:	Fimbres-Castro,Claudia, Álvarez-Borrego,Josué, Vázquez-Martínez,Irene, Espinoza-Carreón,T. Leticia, Ulloa-Pérez,A. Elsi, Bueno-Ibarra,Mario A
Lenguaje:	English
Publicado:	Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción 2013
Materias:	image processing invariant digital system pattern recognition plankton identification
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-65382013000200005
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spelling	oai:scielo:S0717-653820130002000052014-08-18Nonlinear correlation by using invariant identity vectors signatures to identify planktonFimbres-Castro,ClaudiaÁlvarez-Borrego,JosuéVázquez-Martínez,IreneEspinoza-Carreón,T. LeticiaUlloa-Pérez,A. ElsiBueno-Ibarra,Mario A image processing invariant digital system pattern recognition plankton identification In this paper a new methodology to recognize radk>larians is presented. This system is invariant to position, rotation and scale by using identity vectors signatures (Is) obtained for both the target and the problem image. In this application, / is obtained by means of a simplification of the main features of the original image in addition of the properties of the Fourier transform. Identity vectors signatures are compared using nonlinear correlation. This new methodology recognizes objects in a more simple way. It has a low computational cost of approximately 0.02 s per image. In addition, the statistics of Euclidean distances is used as an alternative methodology for comparison of the identity vectors signatures. Also, experiments were carried out in order to find the noise tolerance. The discrimination coefficient was used as a metric in performance evaluation in presence of noise. The invariant to position, rotation and scale of this digital system was tested with 20 different species of radiolarians and with 26 different species of phytoplankton (real images). The results obtained have a confidence level above 95.4%.info:eu-repo/semantics/openAccessFacultad de Ciencias Naturales y Oceanográficas, Universidad de ConcepciónGayana (Concepción) v.77 n.2 20132013-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-65382013000200005en10.4067/S0717-65382013000200005
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	image processing invariant digital system pattern recognition plankton identification
spellingShingle	image processing invariant digital system pattern recognition plankton identification Fimbres-Castro,Claudia Álvarez-Borrego,Josué Vázquez-Martínez,Irene Espinoza-Carreón,T. Leticia Ulloa-Pérez,A. Elsi Bueno-Ibarra,Mario A Nonlinear correlation by using invariant identity vectors signatures to identify plankton
description	In this paper a new methodology to recognize radk>larians is presented. This system is invariant to position, rotation and scale by using identity vectors signatures (Is) obtained for both the target and the problem image. In this application, / is obtained by means of a simplification of the main features of the original image in addition of the properties of the Fourier transform. Identity vectors signatures are compared using nonlinear correlation. This new methodology recognizes objects in a more simple way. It has a low computational cost of approximately 0.02 s per image. In addition, the statistics of Euclidean distances is used as an alternative methodology for comparison of the identity vectors signatures. Also, experiments were carried out in order to find the noise tolerance. The discrimination coefficient was used as a metric in performance evaluation in presence of noise. The invariant to position, rotation and scale of this digital system was tested with 20 different species of radiolarians and with 26 different species of phytoplankton (real images). The results obtained have a confidence level above 95.4%.
author	Fimbres-Castro,Claudia Álvarez-Borrego,Josué Vázquez-Martínez,Irene Espinoza-Carreón,T. Leticia Ulloa-Pérez,A. Elsi Bueno-Ibarra,Mario A
author_facet	Fimbres-Castro,Claudia Álvarez-Borrego,Josué Vázquez-Martínez,Irene Espinoza-Carreón,T. Leticia Ulloa-Pérez,A. Elsi Bueno-Ibarra,Mario A
author_sort	Fimbres-Castro,Claudia
title	Nonlinear correlation by using invariant identity vectors signatures to identify plankton
title_short	Nonlinear correlation by using invariant identity vectors signatures to identify plankton
title_full	Nonlinear correlation by using invariant identity vectors signatures to identify plankton
title_fullStr	Nonlinear correlation by using invariant identity vectors signatures to identify plankton
title_full_unstemmed	Nonlinear correlation by using invariant identity vectors signatures to identify plankton
title_sort	nonlinear correlation by using invariant identity vectors signatures to identify plankton
publisher	Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción
publishDate	2013
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-65382013000200005
work_keys_str_mv	AT fimbrescastroclaudia nonlinearcorrelationbyusinginvariantidentityvectorssignaturestoidentifyplankton AT alvarezborregojosue nonlinearcorrelationbyusinginvariantidentityvectorssignaturestoidentifyplankton AT vazquezmartinezirene nonlinearcorrelationbyusinginvariantidentityvectorssignaturestoidentifyplankton AT espinozacarreontleticia nonlinearcorrelationbyusinginvariantidentityvectorssignaturestoidentifyplankton AT ulloaperezaelsi nonlinearcorrelationbyusinginvariantidentityvectorssignaturestoidentifyplankton AT buenoibarramarioa nonlinearcorrelationbyusinginvariantidentityvectorssignaturestoidentifyplankton
_version_	1718442160382017536

Nonlinear correlation by using invariant identity vectors signatures to identify plankton

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