Publications
2018
1.
Mazo, Claudia; Bernal, Jose; Trujillo, María; Alegre, Enrique
Transfer learning for classification of cardiovascular tissues in histological images Artículo de revista
En: Computer methods and programs in biomedicine, vol. 165, pp. 69–76, 2018, (Publisher: Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: cardiovascular system, Fundamental Tissues, Histological Images, Organs, SVM, Transfer Learning
@article{mazo_transfer_2018,
title = {Transfer learning for classification of cardiovascular tissues in histological images},
author = {Claudia Mazo and Jose Bernal and María Trujillo and Enrique Alegre},
url = {https://www.sciencedirect.com/science/article/pii/S0169260718305297},
year = {2018},
date = {2018-01-01},
journal = {Computer methods and programs in biomedicine},
volume = {165},
pages = {69–76},
abstract = {This paper proposes an automatic method for classifying healthy tissues and organs from histology images using Convolutional Neural Networks (CNNs). The approach aims to address the challenges in automated tissue and organ recognition, which is crucial for educational and medical purposes. By leveraging the powerful capabilities of deep learning, particularly CNNs, the method seeks to improve classification accuracy and efficiency, building on prior advances in image processing and supervised learning.},
note = {Publisher: Elsevier},
keywords = {cardiovascular system, Fundamental Tissues, Histological Images, Organs, SVM, Transfer Learning},
pubstate = {published},
tppubtype = {article}
}
This paper proposes an automatic method for classifying healthy tissues and organs from histology images using Convolutional Neural Networks (CNNs). The approach aims to address the challenges in automated tissue and organ recognition, which is crucial for educational and medical purposes. By leveraging the powerful capabilities of deep learning, particularly CNNs, the method seeks to improve classification accuracy and efficiency, building on prior advances in image processing and supervised learning.
2014
2.
Olivera, Óscar García-Olalla; Alegre, Enrique; Fernández-Robles, Laura; González-Castro, Víctor
Local oriented statistics information booster (LOSIB) for texture classification Artículo de revista
En: 2014 22nd international conference on pattern recognition, pp. 1114–1119, 2014, (Publisher: IEEE).
Resumen | Enlaces | BibTeX | Etiquetas: LBP, LOSIB, SVM, texture descriptors
@article{garcia-olalla_olivera_local_2014,
title = {Local oriented statistics information booster (LOSIB) for texture classification},
author = {Óscar García-Olalla Olivera and Enrique Alegre and Laura Fernández-Robles and Víctor González-Castro},
url = {https://ieeexplore.ieee.org/abstract/document/6976911},
year = {2014},
date = {2014-01-01},
journal = {2014 22nd international conference on pattern recognition},
pages = {1114–1119},
abstract = {This paper presents the Local Oriented Statistical Information Booster (LOSIB), a descriptor enhancer that extracts gray level differences along multiple orientations. By combining LOSIB with classical descriptors like WCF4 (Wavelet Co-occurrence Features) and LBP (Local Binary Pattern), classification performance is improved on the KTH-Tips-2a and Brodatz32 datasets. Results show improvements of up to 16.94% on KTH and 7.55% on Brodatz using SVM. Additionally, combining LOSIB with CLBP (Complete LBP) also enhances performance on both datasets.},
note = {Publisher: IEEE},
keywords = {LBP, LOSIB, SVM, texture descriptors},
pubstate = {published},
tppubtype = {article}
}
This paper presents the Local Oriented Statistical Information Booster (LOSIB), a descriptor enhancer that extracts gray level differences along multiple orientations. By combining LOSIB with classical descriptors like WCF4 (Wavelet Co-occurrence Features) and LBP (Local Binary Pattern), classification performance is improved on the KTH-Tips-2a and Brodatz32 datasets. Results show improvements of up to 16.94% on KTH and 7.55% on Brodatz using SVM. Additionally, combining LOSIB with CLBP (Complete LBP) also enhances performance on both datasets.
2012
3.
Fernández-Robles, Laura; Olivera, Óscar García-Olalla; García-Ordás, Maite; García-Ordás, Diego; Alegre, Enrique
Svm approach to classify boar acrosome integrity of a multi-features surf description Artículo de revista
En: Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012, pp. 121, 2012, (Publisher: Universidade de Vigo).
Resumen | Enlaces | BibTeX | Etiquetas: acrosome integrity, Multi-feature Classification, SVM
@article{fernandez-robles_svm_2012,
title = {Svm approach to classify boar acrosome integrity of a multi-features surf description},
author = {Laura Fernández-Robles and Óscar García-Olalla Olivera and Maite García-Ordás and Diego García-Ordás and Enrique Alegre},
url = {https://dialnet.unirioja.es/servlet/articulo?codigo=8886078},
year = {2012},
date = {2012-01-01},
journal = {Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012},
pages = {121},
abstract = {Actas de las XXXIII Jornadas de Automática},
note = {Publisher: Universidade de Vigo},
keywords = {acrosome integrity, Multi-feature Classification, SVM},
pubstate = {published},
tppubtype = {article}
}
Actas de las XXXIII Jornadas de Automática
4.
García-Ordás, Maite; Fernández-Robles, Laura; Olivera, Óscar García-Olalla; García-Ordás, Diego; Alegre, Enrique
Boar spermatozoa classication using local invariant features and bag ofwords Artículo de revista
En: Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012, pp. 124, 2012, (Publisher: Universidade de Vigo).
Resumen | Enlaces | BibTeX | Etiquetas: Bag of Words, Image classification, Invariant Local Features, SVM
@article{garcia-ordas_boar_2012,
title = {Boar spermatozoa classication using local invariant features and bag ofwords},
author = {Maite García-Ordás and Laura Fernández-Robles and Óscar García-Olalla Olivera and Diego García-Ordás and Enrique Alegre},
url = {https://d1wqtxts1xzle7.cloudfront.net/44449820/Boar_spermatozoa_classification_using_lo20160405-12762-rpptl6-libre.pdf?1459894371=&response-content-disposition=inline%3B+filename%3DBoar_spermatozoa_classification_using_lo.pdf&Expires=1739810149&Signature=TVTQuev93pbuKg4OlXk4suOi~Coac8HAB8rlkx~gQU1hgQGzVLSHM-qPjGgmrebUZtRI6cO92VmqX5nLYwJZXXqabj7XL~MZdxEyfZFsXefB2yEW47E37QamibGNRwNQOYYXsLMkBcV4yjY0~fk4eEh3muwznGtFmBzYynLuFUsE6eDRmhg3caXHnwOE3ulYfilE-VRBGlORpq-q7c6UMS8rsvmj9L1PvOjwno77xYKcgu5Hf0wOgFkKgIZx-XLJ39pzh2pxzdiiLz7Ghnwmre5XjiqAR6wheQfEey~tzH31B5aewIWwQfYy4FAniapRIv2t~8i9uanYGwwG0ZMu4w__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2012},
date = {2012-01-01},
journal = {Actas de las XXXIII Jornadas de Automática: Vigo, 5 al 7 de Septiembre de 2012},
pages = {124},
abstract = {In this work, different descriptors and classifiers were compared to classify boar spermatozoa acrosome as intact or damaged using the Bag of Words (BOW) method. This approach models images using a dictionary-based technique, where each image is described by local points from the dictionary without considering spatial information. The method was tested with SVM, kNN, QDA, and LDA classifiers. The dictionary was created using two approaches: k-means and fuzzy clustering. Better results were obtained with the k-means algorithm and SVM classification. Two local invariant descriptors were tested: SIFT with a success rate of 64.88% and SURF with a success rate of 71.75%.},
note = {Publisher: Universidade de Vigo},
keywords = {Bag of Words, Image classification, Invariant Local Features, SVM},
pubstate = {published},
tppubtype = {article}
}
In this work, different descriptors and classifiers were compared to classify boar spermatozoa acrosome as intact or damaged using the Bag of Words (BOW) method. This approach models images using a dictionary-based technique, where each image is described by local points from the dictionary without considering spatial information. The method was tested with SVM, kNN, QDA, and LDA classifiers. The dictionary was created using two approaches: k-means and fuzzy clustering. Better results were obtained with the k-means algorithm and SVM classification. Two local invariant descriptors were tested: SIFT with a success rate of 64.88% and SURF with a success rate of 71.75%.
2007
5.
González, Maribel; Alegre, Enrique; Alaiz-Rodríguez, Rocío; Sánchez-González, Lidia
Acrosome integrity classification of boar spermatozoon images using dwt and texture descriptors Artículo de revista
En: Computational Vision and Medical Image Processing: VipIMAGE, vol. 2007, 2007.
Resumen | Enlaces | BibTeX | Etiquetas: acrosome integrity, contour description, early fusion, fourier shape descriptor, LBP, SVM, texture description, wavelet
@article{gonzalez_acrosome_2007,
title = {Acrosome integrity classification of boar spermatozoon images using dwt and texture descriptors},
author = {Maribel González and Enrique Alegre and Rocío Alaiz-Rodríguez and Lidia Sánchez-González},
url = {https://d1wqtxts1xzle7.cloudfront.net/44449828/Acrosome_integrity_assessment_of_boar_sp20160405-3183-jdniiw-libre.pdf?1459894375=&response-content-disposition=inline%3B+filename%3DAcrosome_integrity_assessment_of_boar_sp.pdf&Expires=1738604441&Signature=gCki53PLr5Uqz1IsOZ87L788ljr1cPDhvd3XAIPiXXJyhy7gT1U0WIFjenpIGpKsNIg1lei0Y9wxLIssqiUqYYi2BrXDLX8qxHOSVnNjAj8bmBUVTWeiFHnHvMPbg–6ZzHG71Dj0RkarOCf1~C~OvGTQbjmSLusV5afdpSCJRuBd2eVbcFy4NGFcTSRMxPZGwJO-t87Aheo846zp-rUxOlkSN4YluJiuov6VhGnufaa4PfmwWgMSUxod9HGpYagpjvzk~RT24b73pZKITpgQqaeIrgE9O27~kYabvFZ3wWr8c0wEmGIBbj8doWz59ibPXF7GBaIzZXZj1TyWyvbQA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
year = {2007},
date = {2007-01-01},
journal = {Computational Vision and Medical Image Processing: VipIMAGE},
volume = {2007},
abstract = {This study focuses on classifying boar sperm as acrosome-intact or acrosome-damaged using grayscale images from phase-contrast microscopy. By combining shape and texture descriptors with a Support Vector Machine (SVM), the authors achieve an F-Score of 0.9913, outperforming previous methods. This work highlights the importance of sperm head contour information and the effectiveness of early fusion techniques in sperm classification, making it a significant advancement in the field.},
keywords = {acrosome integrity, contour description, early fusion, fourier shape descriptor, LBP, SVM, texture description, wavelet},
pubstate = {published},
tppubtype = {article}
}
This study focuses on classifying boar sperm as acrosome-intact or acrosome-damaged using grayscale images from phase-contrast microscopy. By combining shape and texture descriptors with a Support Vector Machine (SVM), the authors achieve an F-Score of 0.9913, outperforming previous methods. This work highlights the importance of sperm head contour information and the effectiveness of early fusion techniques in sperm classification, making it a significant advancement in the field.
0000
6.
Olivera, Óscar García-Olalla; Alegre, Enrique; Barreiro, Joaquín; Fernández-Robles, Laura; García-Ordás, María Teresa
Tool wear classification using texture descriptors based on Local Binary Pattern Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: Machine Learing, Manufacturing, SVM, texture analysis, Tool wear
@article{garcia-olalla_tool_nodate,
title = {Tool wear classification using texture descriptors based on Local Binary Pattern},
author = {Óscar García-Olalla Olivera and Enrique Alegre and Joaquín Barreiro and Laura Fernández-Robles and María Teresa García-Ordás},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=opCbArQAAAAJ&cstart=100&pagesize=100&sortby=title&citation_for_view=opCbArQAAAAJ:RHpTSmoSYBkC},
abstract = {This paper presents a new approach for tool wear identification in metal milling using texture descriptors LBP and ALBP, enhanced with LOSIB (Local Oriented Statistical Information Booster). Two datasets are considered: Cutting Edges (gray-scale images of worn cutting edges) and Edge Wear (cropped worn areas). Both datasets are labeled into two (low/high wear) and three (low/medium/high wear) classes. Classification using Support Vector Machine with Least Squares training achieved a 74.05% accuracy for two classes and 53.77% for three classes in the Edge Wear dataset.},
keywords = {Machine Learing, Manufacturing, SVM, texture analysis, Tool wear},
pubstate = {published},
tppubtype = {article}
}
This paper presents a new approach for tool wear identification in metal milling using texture descriptors LBP and ALBP, enhanced with LOSIB (Local Oriented Statistical Information Booster). Two datasets are considered: Cutting Edges (gray-scale images of worn cutting edges) and Edge Wear (cropped worn areas). Both datasets are labeled into two (low/high wear) and three (low/medium/high wear) classes. Classification using Support Vector Machine with Least Squares training achieved a 74.05% accuracy for two classes and 53.77% for three classes in the Edge Wear dataset.