Publications

@article{garcia-olalla_descripcion_2016,

title = {Descripción de textura en imágenes utilizando local Binary Pattern (LBP)},

author = {Óscar García-Olalla Olivera and Enrique Alegre},

url = {https://portalcienciaytecnologia.jcyl.es/documentos/63533728978f296ba7a95f3f},

year  = {2016},

date = {2016-01-01},

urldate = {2016-01-01},

journal = {Conceptos y métodos en visión por computador},

pages = {115–130},

abstract = {Este capítulo estudia el descriptor de textura Local Binary Pattern (LBP), ampliamente utilizado en visión por computador. Se introduce el concepto de textura en imágenes digitales, seguido de una explicación detallada sobre el LBP y sus variaciones, como ALBP, LBPV y CLBP. Además, se discuten algunas de sus aplicaciones.},

note = {Publisher: Grupo de Visión del Comité Español de Automática (CEA)},

keywords = {Descriptores de Textura, LBP, local binary pattern, Visión por Computador},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_tool_2015,

title = {Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers},

author = {Óscar García-Olalla Olivera and Enrique Alegre and Joaquín Barreiro and Laura Fernández-Robles and María Teresa García-Ordás},

url = {https://www.sciencedirect.com/science/article/pii/S187770581504494X},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Procedia engineering},

volume = {132},

pages = {950–957},

abstract = {This paper presents an automatic tool wear detection method using computer vision and texture recognition. Two LBP-based methods combined with the LOSIB texture booster were evaluated on a dataset of 577 images. Binary (Low-High) and ternary (Low-Medium-High) classifications were performed, achieving 80.58% and 67.76% accuracy, respectively. Results highlight the potential for cost and time savings in industrial tool condition monitoring systems (TCMS).},

note = {Publisher: No longer published by Elsevier},

keywords = {LBP, LOSIB, Monitoring, TCM, texture description, Tool wear},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_texture_2015,

title = {Texture descriptors based on adaptive neighborhoods for classification of pigmented skin lesions},

author = {Víctor González-Castro and Johan Debayle and Yanal Wazaefi and Mehdi Rahim and Caroline Gaudy-Marqueste and Jean-Jacques Grob and Bernard Fertil},

url = {https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-24/issue-6/061104/Texture-descriptors-based-on-adaptive-neighborhoods-for-classification-of-pigmented/10.1117/1.JEI.24.6.061104.short},

year  = {2015},

date = {2015-01-01},

journal = {Journal of Electronic Imaging},

volume = {24},

number = {6},

pages = {061104–061104},

abstract = {This paper proposes two texture descriptors for the automatic classification of skin lesions from dermoscopic images, focusing on color texture analysis. The first descriptor uses adaptive mathematical morphology (MM) and Kohonen self-organizing maps (SOM), while the second uses local binary patterns (LBP) with adaptive neighborhoods. Neither approach requires prior segmentation. The results show that the adaptive neighborhood-based LBP approach outperforms both nonadaptive versions of the proposed descriptors and dermatologists' visual predictions, as confirmed by receiver operating characteristic analysis.},

note = {Publisher: Society of Photo-Optical Instrumentation Engineers},

keywords = {Adaptive Neighborhoods, Dermoscopic Images, LBP, Local Binary Patterns, skin lesion classification, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_automatic_2015-1,

title = {Automatic classification of skin lesions using geometrical measurements of adaptive neighborhoods and local binary patterns},

author = {Víctor González-Castro and Johan Debayle and Yanal Wazaefi and Mehdi Rahim and Caroline Gaudy-Marqueste and Jean-Jacques Grob and Bernard Fertil},

url = {https://ieeexplore.ieee.org/abstract/document/7351095},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {2015 IEEE International Conference on Image Processing (ICIP)},

pages = {1722–1726},

abstract = {This paper presents a method for characterizing and classifying skin lesions in dermoscopic images to detect melanoma. It uses Local Binary Patterns (LBPs) on geometrical feature maps derived from General Adaptive Neighborhoods (GAN). An Artificial Neural Network evaluates performance, showing that the GAN-based approach outperforms classical LBPs and dermatologists' predictions in ROC curve analysis.},

note = {Publisher: IEEE},

keywords = {dermoscopic imaging, LBP, local binary pattern, skin cancer detection},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{garcia-olalla_olivera_evaluation_2013,

title = {Evaluation of LBP Variants using several Metrics and kNN Classifiers},

author = {Óscar García-Olalla Olivera and Enrique Alegre and María Teresa García-Ordás and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-642-41062-8_15},

year  = {2013},

date = {2013-01-01},

journal = {Similarity Search and Applications: 6th International Conference, SISAP 2013, A Coruña, Spain, October 2-4, 2013, Proceedings 6},

pages = {151–162},

abstract = {Este estudio compara ALBPS con LBP y sus variantes en recuperación de imágenes, utilizando los conjuntos de datos KTH-TIPS 2a y un conjunto de esperma para clasificación de vitalidad. ALBPS superó a los otros métodos, logrando un 61,47% de acierto en KTH-TIPS 2a y 72,66% en el conjunto de esperma, destacando su mayor poder discriminante.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {ALBPS, Análisis de Textura, kNN, LBP, Reconocimiento de Materiales, Recuperación de imágenes},

pubstate = {published},

tppubtype = {article}

}

@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}

}