Publications
2016
1.
Fidalgo, Eduardo; Alegre, Enrique; González-Castro, Víctor; Fernández-Robles, Laura
Compass radius estimation for improved image classification using Edge-SIFT Artículo de revista
En: Neurocomputing, vol. 197, pp. 119–135, 2016, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: Accuracy Improvement, Compass operator, Edge-SIFT, Image classification, SIFT
@article{fidalgo_compass_2016,
title = {Compass radius estimation for improved image classification using Edge-SIFT},
author = {Eduardo Fidalgo and Enrique Alegre and Víctor González-Castro and Laura Fernández-Robles},
url = {https://www.sciencedirect.com/science/article/pii/S0925231216002824},
year = {2016},
date = {2016-01-01},
journal = {Neurocomputing},
volume = {197},
pages = {119–135},
abstract = {Combining SIFT with Edge-SIFT enhances image classification. This study evaluates how different radii of the compass operator impact performance and shows that the commonly used radius of 4.00 is not optimal. By selecting the best radius for each image, accuracy can exceed 95%. A new method is proposed to determine the optimal radius, leading to accuracy improvements across several datasets, with gains up to 24.4%.},
note = {Publisher: Elsevier},
keywords = {Accuracy Improvement, Compass operator, Edge-SIFT, Image classification, SIFT},
pubstate = {published},
tppubtype = {article}
}
Combining SIFT with Edge-SIFT enhances image classification. This study evaluates how different radii of the compass operator impact performance and shows that the commonly used radius of 4.00 is not optimal. By selecting the best radius for each image, accuracy can exceed 95%. A new method is proposed to determine the optimal radius, leading to accuracy improvements across several datasets, with gains up to 24.4%.
2.
Alegre, Enrique; Fernández-Robles, Laura
SIFT (Scale Invariant Feature Transform) Artículo de revista
En: Conceptos y métodos en visión por computador, pp. 131–158, 2016, (Publisher: Grupo de Visión del Comité Español de Automática (CEA)).
Resumen | Enlaces | BibTeX | Etiquetas: Descriptores, Invarianza, Reconocimiento de objetos, SIFT
@article{alegre_sift_2016,
title = {SIFT (Scale Invariant Feature Transform)},
author = {Enrique Alegre and Laura Fernández-Robles},
url = {https://buleria.unileon.es/handle/10612/11065},
year = {2016},
date = {2016-01-01},
journal = {Conceptos y métodos en visión por computador},
pages = {131–158},
abstract = {SIFT es un método que detecta puntos clave en imágenes y los describe mediante un histograma de gradientes, siendo invariante a la escala, posición y orientación. Cada punto se define con un vector de 128 elementos que contiene su posición, escala y orientación. El capítulo explica cómo obtener estos descriptores y su uso en el reconocimiento de objetos, además de mencionar extensiones y descriptores relacionados.},
note = {Publisher: Grupo de Visión del Comité Español de Automática (CEA)},
keywords = {Descriptores, Invarianza, Reconocimiento de objetos, SIFT},
pubstate = {published},
tppubtype = {article}
}
SIFT es un método que detecta puntos clave en imágenes y los describe mediante un histograma de gradientes, siendo invariante a la escala, posición y orientación. Cada punto se define con un vector de 128 elementos que contiene su posición, escala y orientación. El capítulo explica cómo obtener estos descriptores y su uso en el reconocimiento de objetos, además de mencionar extensiones y descriptores relacionados.
3.
Alegre, Enrique; Fernández-Robles, Laura
SIFT (Scale Invariant Feature Transform) Artículo de revista
En: Conceptos y métodos en visión por computador, pp. 131–158, 2016, (Publisher: Grupo de Visión del Comité Español de Automática (CEA)).
Resumen | Enlaces | BibTeX | Etiquetas: Descriptores, Reconocimiento de objetos, SIFT
@article{alegre_sift_2016-1,
title = {SIFT (Scale Invariant Feature Transform)},
author = {Enrique Alegre and Laura Fernández-Robles},
url = {https://portalcientifico.unileon.es/documentos/63533728978f296ba7a95f4d?lang=de},
year = {2016},
date = {2016-01-01},
journal = {Conceptos y métodos en visión por computador},
pages = {131–158},
abstract = {SIFT es un método que detecta puntos clave en imágenes y los describe mediante un histograma de gradientes, siendo invariante a la escala, posición y orientación. Cada punto se define con un vector de 128 elementos que contiene su posición, escala y orientación. El capítulo explica cómo obtener estos descriptores y su uso en el reconocimiento de objetos, además de mencionar extensiones y descriptores relacionados.},
note = {Publisher: Grupo de Visión del Comité Español de Automática (CEA)},
keywords = {Descriptores, Reconocimiento de objetos, SIFT},
pubstate = {published},
tppubtype = {article}
}
SIFT es un método que detecta puntos clave en imágenes y los describe mediante un histograma de gradientes, siendo invariante a la escala, posición y orientación. Cada punto se define con un vector de 128 elementos que contiene su posición, escala y orientación. El capítulo explica cómo obtener estos descriptores y su uso en el reconocimiento de objetos, además de mencionar extensiones y descriptores relacionados.
2015
4.
Fernández-Robles, Laura; Castejón-Limas, Manuel; Alfonso-Cendón, Javier; Alegre, Enrique
Evaluation of clustering configurations for object retrieval using sift features Artículo de revista
En: Project Management and Engineering: Selected Papers from the 17th International AEIPRO Congress held in Logroño, Spain, in 2013, pp. 279–291, 2015, (Publisher: Springer International Publishing).
Resumen | Enlaces | BibTeX | Etiquetas: ASASEC, Clustering, object recognition, SIFT
@article{fernandez-robles_evaluation_2015,
title = {Evaluation of clustering configurations for object retrieval using sift features},
author = {Laura Fernández-Robles and Manuel Castejón-Limas and Javier Alfonso-Cendón and Enrique Alegre},
url = {https://link.springer.com/chapter/10.1007/978-3-319-12754-5_21},
year = {2015},
date = {2015-01-01},
journal = {Project Management and Engineering: Selected Papers from the 17th International AEIPRO Congress held in Logroño, Spain, in 2013},
pages = {279–291},
abstract = {SIFT (Scale-Invariant Feature Transform) is a widely used keypoint descriptor for robust image matching and object recognition. It identifies distinctive features and matches them using a nearest-neighbor algorithm, followed by clustering with a Hough transform and pose verification via least-squares. However, the clustering choice lacks theoretical justification. This study explores and evaluates alternative clustering configurations based on keypoint pose parameters (x, y location, scale, and orientation).},
note = {Publisher: Springer International Publishing},
keywords = {ASASEC, Clustering, object recognition, SIFT},
pubstate = {published},
tppubtype = {article}
}
SIFT (Scale-Invariant Feature Transform) is a widely used keypoint descriptor for robust image matching and object recognition. It identifies distinctive features and matches them using a nearest-neighbor algorithm, followed by clustering with a Hough transform and pose verification via least-squares. However, the clustering choice lacks theoretical justification. This study explores and evaluates alternative clustering configurations based on keypoint pose parameters (x, y location, scale, and orientation).
5.
Fernández-Robles, Laura; Alfonso-Cendón, Javier; Castejón-Limas, Manuel; Olivera, Óscar García-Olalla; Alegre, Enrique
DESARROLLO DE UNA APLICACIÓN DE RECUPERACIÓN DE OBJETOS Y ANÁLISIS DE LOS DESCRIPTORES LOCALES INVARIANTES UTILIZADOS Artículo de revista
En: 2015.
Resumen | Enlaces | BibTeX | Etiquetas: image analysis, Object Retrieval, SIFT
@article{fernandez-robles_desarrollo_2015,
title = {DESARROLLO DE UNA APLICACIÓN DE RECUPERACIÓN DE OBJETOS Y ANÁLISIS DE LOS DESCRIPTORES LOCALES INVARIANTES UTILIZADOS},
author = {Laura Fernández-Robles and Javier Alfonso-Cendón and Manuel Castejón-Limas and Óscar García-Olalla Olivera and Enrique Alegre},
url = {http://dspace.aeipro.com/xmlui/handle/123456789/725},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
abstract = {This paper presents an application for the ASASEC project that helps retrieve objects from large image and video datasets related to child exploitation cases. By selecting regions of interest and using descriptors like SIFT and COSFIRE, the application finds similar objects across datasets. Experiments showed SIFT and COSFIRE outperformed SURF and HOG in retrieval accuracy.},
keywords = {image analysis, Object Retrieval, SIFT},
pubstate = {published},
tppubtype = {article}
}
This paper presents an application for the ASASEC project that helps retrieve objects from large image and video datasets related to child exploitation cases. By selecting regions of interest and using descriptors like SIFT and COSFIRE, the application finds similar objects across datasets. Experiments showed SIFT and COSFIRE outperformed SURF and HOG in retrieval accuracy.
2011
6.
Fernández-Robles, Laura; García-Ordás, Maite; García-Ordás, Diego; Olivera, Óscar García-Olalla; Alegre, Enrique
Acrosome evaluation of spermatozoa cells using sift and classical texture descriptors Artículo de revista
En: Actas de las XXXII Jornadas de Automática, Escuela Técnica Superior de Ingeniería, Univesidad de Sevilla: Sevilla, 7 al 9 de septiembre de 2011, pp. 84, 2011, (Publisher: Universidad de Sevilla).
Resumen | Enlaces | BibTeX | Etiquetas: acrosome integrity, image processing, SIFT, Sperm Analysis, veterinary science
@article{fernandez-robles_acrosome_2011,
title = {Acrosome evaluation of spermatozoa cells using sift and classical texture descriptors},
author = {Laura Fernández-Robles and Maite García-Ordás and Diego García-Ordás and Óscar García-Olalla Olivera and Enrique Alegre},
url = {https://portalcientifico.unileon.es/documentos/6660aac4241b8f26a79c807a},
year = {2011},
date = {2011-01-01},
journal = {Actas de las XXXII Jornadas de Automática, Escuela Técnica Superior de Ingeniería, Univesidad de Sevilla: Sevilla, 7 al 9 de septiembre de 2011},
pages = {84},
abstract = {Automatic assessment of sperm quality is an important
challenge in the veterinary field. In this
paper, we explore how to best describe the acrosomes
of boar spermatozoa using image analysis
to automatically classify them as intact or damaged.
Our proposal is to characterize the acrosomes
in terms of their membrane integrity using
texture descriptors and compare them with descriptors
based on local invariant features, particularly,
Scale Invariant Feature Transform (SIFT)
method. On the one hand, we use Zernike moments
and Haralick features extracted from the
original image and from the coefficients of the Discrete
Wavelet Transform. On the other hand, the
heads’ features are distinctively described by SIFT,
a method based on detecting local points of interest.
Classification using kNN shows that the best
results were obtained by SIFT, with an overall hit
rate of 84.64% and, what is more important, a
higher hit rate in the damaged (92.96%) than in
the intact class (76.15%). These results make this
descriptor very attractive for the veterinary community.},
note = {Publisher: Universidad de Sevilla},
keywords = {acrosome integrity, image processing, SIFT, Sperm Analysis, veterinary science},
pubstate = {published},
tppubtype = {article}
}
Automatic assessment of sperm quality is an important
challenge in the veterinary field. In this
paper, we explore how to best describe the acrosomes
of boar spermatozoa using image analysis
to automatically classify them as intact or damaged.
Our proposal is to characterize the acrosomes
in terms of their membrane integrity using
texture descriptors and compare them with descriptors
based on local invariant features, particularly,
Scale Invariant Feature Transform (SIFT)
method. On the one hand, we use Zernike moments
and Haralick features extracted from the
original image and from the coefficients of the Discrete
Wavelet Transform. On the other hand, the
heads’ features are distinctively described by SIFT,
a method based on detecting local points of interest.
Classification using kNN shows that the best
results were obtained by SIFT, with an overall hit
rate of 84.64% and, what is more important, a
higher hit rate in the damaged (92.96%) than in
the intact class (76.15%). These results make this
descriptor very attractive for the veterinary community.
challenge in the veterinary field. In this
paper, we explore how to best describe the acrosomes
of boar spermatozoa using image analysis
to automatically classify them as intact or damaged.
Our proposal is to characterize the acrosomes
in terms of their membrane integrity using
texture descriptors and compare them with descriptors
based on local invariant features, particularly,
Scale Invariant Feature Transform (SIFT)
method. On the one hand, we use Zernike moments
and Haralick features extracted from the
original image and from the coefficients of the Discrete
Wavelet Transform. On the other hand, the
heads’ features are distinctively described by SIFT,
a method based on detecting local points of interest.
Classification using kNN shows that the best
results were obtained by SIFT, with an overall hit
rate of 84.64% and, what is more important, a
higher hit rate in the damaged (92.96%) than in
the intact class (76.15%). These results make this
descriptor very attractive for the veterinary community.