An image processing pipeline to segment iris for unconstrained cow identification system

Autores
Larregui, Juan Ignacio; Cazzato, Dario; Castro, Silvia Mabel
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.
Fil: Larregui, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
Fil: Cazzato, Dario. : University Of Luxembourg; Luxemburgo. Interdisciplinary Centre For Security Reliability And T; Luxemburgo
Fil: Castro, Silvia Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
Materia
BOVINE EYE
IMAGE PROCESSING
IRIS SEGMENTATION
PUPIL SEGMENTATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/135193
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling An image processing pipeline to segment iris for unconstrained cow identification systemLarregui, Juan IgnacioCazzato, DarioCastro, Silvia MabelBOVINE EYEIMAGE PROCESSINGIRIS SEGMENTATIONPUPIL SEGMENTATIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.Fil: Larregui, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaFil: Cazzato, Dario. : University Of Luxembourg; Luxemburgo. Interdisciplinary Centre For Security Reliability And T; LuxemburgoFil: Castro, Silvia Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; ArgentinaDe Gruyter2019-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/135193Larregui, Juan Ignacio; Cazzato, Dario; Castro, Silvia Mabel; An image processing pipeline to segment iris for unconstrained cow identification system; De Gruyter; Open Computer Science; 9; 1; 1-2019; 145-1592299-10932299-1093CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.degruyter.com/view/journals/comp/9/1/article-p145.xmlinfo:eu-repo/semantics/altIdentifier/doi/10.1515/comp-2019-0010info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:42Zoai:ri.conicet.gov.ar:11336/135193instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:32:42.811CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv An image processing pipeline to segment iris for unconstrained cow identification system
title An image processing pipeline to segment iris for unconstrained cow identification system
spellingShingle An image processing pipeline to segment iris for unconstrained cow identification system
Larregui, Juan Ignacio
BOVINE EYE
IMAGE PROCESSING
IRIS SEGMENTATION
PUPIL SEGMENTATION
title_short An image processing pipeline to segment iris for unconstrained cow identification system
title_full An image processing pipeline to segment iris for unconstrained cow identification system
title_fullStr An image processing pipeline to segment iris for unconstrained cow identification system
title_full_unstemmed An image processing pipeline to segment iris for unconstrained cow identification system
title_sort An image processing pipeline to segment iris for unconstrained cow identification system
dc.creator.none.fl_str_mv Larregui, Juan Ignacio
Cazzato, Dario
Castro, Silvia Mabel
author Larregui, Juan Ignacio
author_facet Larregui, Juan Ignacio
Cazzato, Dario
Castro, Silvia Mabel
author_role author
author2 Cazzato, Dario
Castro, Silvia Mabel
author2_role author
author
dc.subject.none.fl_str_mv BOVINE EYE
IMAGE PROCESSING
IRIS SEGMENTATION
PUPIL SEGMENTATION
topic BOVINE EYE
IMAGE PROCESSING
IRIS SEGMENTATION
PUPIL SEGMENTATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.
Fil: Larregui, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
Fil: Cazzato, Dario. : University Of Luxembourg; Luxemburgo. Interdisciplinary Centre For Security Reliability And T; Luxemburgo
Fil: Castro, Silvia Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
description One of the most evident costs in cow farming is the identification of the animals. Classic identification processes are labour-intensive, prone to human errors and invasive for the animal. An automated alternative is an animal identification based on unique biometric patterns like iris recognition; in this context, correct segmentation of the region of interest becomes of critical importance. This work introduces a bovine iris segmentation pipeline that processes images taken in the wild, extracting the iris region. The solution deals with images taken with a regular visible-light camera in real scenarios, where reflections in the iris and camera flash introduce a high level of noise that makes the segmentation procedure challenging. Traditional segmentation techniques for the human iris are not applicable given the nature of the bovine eye; at this aim, a dataset composed of catalogued images and manually labelled ground truth data of Aberdeen-Angus has been used for the experiments and made publicly available. The unique ID number for each different animal in the dataset is provided, making it suitable for recognition tasks. Segmentation results have been validated with our dataset showing high reliability: with the most pessimistic metric (i.e. intersection over union), a mean score of 0.8957 has been obtained.
publishDate 2019
dc.date.none.fl_str_mv 2019-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/135193
Larregui, Juan Ignacio; Cazzato, Dario; Castro, Silvia Mabel; An image processing pipeline to segment iris for unconstrained cow identification system; De Gruyter; Open Computer Science; 9; 1; 1-2019; 145-159
2299-1093
2299-1093
CONICET Digital
CONICET
url http://hdl.handle.net/11336/135193
identifier_str_mv Larregui, Juan Ignacio; Cazzato, Dario; Castro, Silvia Mabel; An image processing pipeline to segment iris for unconstrained cow identification system; De Gruyter; Open Computer Science; 9; 1; 1-2019; 145-159
2299-1093
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.degruyter.com/view/journals/comp/9/1/article-p145.xml
info:eu-repo/semantics/altIdentifier/doi/10.1515/comp-2019-0010
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv De Gruyter
publisher.none.fl_str_mv De Gruyter
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.069144

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