Publications

@article{sanchez-gonzalez_local_2020,

title = {Local binary pattern features to detect anomalies in machined workpiece},

author = {Lidia Sánchez-González and Virginia Riego and Manuel Castejón-Limas and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-030-61705-9_55},

year  = {2020},

date = {2020-01-01},

journal = {International Conference on Hybrid Artificial Intelligence Systems},

pages = {665–673},

abstract = {This paper proposes a vision-based system for evaluating the surface finish of machined workpieces by using Local Binary Pattern (LBP) vectors to represent image textures. The system detects wear on surfaces by analyzing the texture descriptors, as regular patterns correspond to unworn surfaces. Four classification techniques are tested, with the Random Forest algorithm achieving the highest accuracy of 86.0%, meeting the expert requirements for quality control.},

note = {Publisher: Springer International Publishing Cham},

keywords = {local binary pattern, Random Forest Classification, Surface Finish, Wear Detection},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_olivera_evaluation_2017,

title = {Evaluation of the State of Cutting Tools According to Its Texture Using LOSIB and LBP Variants},

author = {Óscar García-Olalla Olivera and Laura Fernández-Robles and Eduardo Fidalgo and Víctor González-Castro and Enrique Alegre},

url = {https://link.springer.com/chapter/10.1007/978-3-319-51859-6_15},

year  = {2017},

date = {2017-01-01},

journal = {Project Management and Engineering Research: AEIPRO 2016},

pages = {217–228},

abstract = {The FRESVIDA project focuses on assessing the lifespan of cutting tools under extreme conditions using digital image processing. It evaluates various texture descriptors based on Local Binary Patterns (LBP), including variants like LBPV and DLBPCS, using the Outex dataset. The descriptors are tested with Support Vector Machines (SVM), and results show that combining them with LOSIB reduces performance due to the dataset’s emphasis on rotation invariance.},

note = {Publisher: Springer International Publishing},

keywords = {Computer vision, Cutting Tools, local binary pattern, Texture},

pubstate = {published},

tppubtype = {article}

}

@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{gonzalez-castro_texture-based_2016,

title = {Texture-based classification for the automatic rating of the perivascular spaces in brain MRI},

author = {Víctor González-Castro and María del Carmen Valdés-Hernández and Paul A Armitage and Joanna M Wardlaw},

url = {https://www.sciencedirect.com/science/article/pii/S1877050916311802},

year  = {2016},

date = {2016-01-01},

journal = {Procedia Computer Science},

volume = {90},

pages = {9–14},

abstract = {This paper investigates the classification of enlarged perivascular spaces (PVS) in the basal ganglia (BG) using texture features extracted from structural brain MRI. The texture is described through first-order statistics, co-occurrence matrix features, discrete wavelet transform coefficients (WSF and WCF), and local binary patterns (LBP). The texture features are classified using a Support Vector Machine (SVM). Experimental results show that WCF provides an accuracy of 80.03% in classifying the density of enlarged PVS.},

note = {Publisher: Elsevier},

keywords = {Discrete Wavelet Transform, local binary pattern, perivascular spaces, support vector machine, 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_vitality_2013,

title = {Vitality assessment of boar sperm using an adaptive LBP based on oriented deviation},

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

url = {https://link.springer.com/chapter/10.1007/978-3-642-37410-4_6},

year  = {2013},

date = {2013-01-01},

journal = {Computer Vision-ACCV 2012 Workshops: ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012, Revised Selected Papers, Part I 11},

pages = {61–72},

abstract = {This paper proposes a new method to assess sperm vitality using a hybrid combination of local and global texture descriptors. A new adaptive local binary pattern (ALBP) descriptor, enhanced with oriented standard deviation information, is introduced as ALBPS, achieving better performance in sperm vitality assessment with an accuracy of 81.88%. In addition, the global description of sperm heads was evaluated with various classical texture algorithms, where the combination of Wavelet transform and Haralick feature extraction (WCF13) yielded the best results. The hybrid approach combining ALBPS and WCF13 achieved superior performance, with the best result being a F-Score of 0.886 and an accuracy of 85.63%. This method represents a significant improvement in sperm vitality classification.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {local binary pattern, Sperm Vitality, support vector machine, texture descriptors},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_olivera_adaptive_2013,

title = {Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification},

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

url = {https://link.springer.com/article/10.1186/1687-5281-2013-31},

year  = {2013},

date = {2013-01-01},

journal = {EURASIP journal on image and video processing},

volume = {2013},

pages = {1–11},

abstract = {This paper proposes a new texture description method combining local and global texture descriptors for image classification. The adaptive local binary pattern with oriented standard deviation (ALBPS) method provides enhanced local features, while the global description uses a wavelet transform-based descriptor, WCF13. These descriptors were combined with a support vector machine for classification, yielding high accuracy (85.63%) and F-score (0.886) for spermatozoa data and good results (84.45%) for the KTH-TIPS 2a dataset. The hybrid approach outperformed previous methods.},

note = {Publisher: Springer International Publishing},

keywords = {adaptive local binary pattern, hybrid feature extraction, image analysis, local binary pattern, spermatozoa assessment, support vector machine, texture classification, wavelet trasform},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_vitality_2012,

title = {Vitality assessment of boar sperm using n concentric squares resized and local binary pattern in gray scale images},

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

url = {https://www.researchgate.net/profile/Oscar-Garcia-Olalla/publication/268519830_Vitality_assessment_of_boar_sperm_using_N_Concentric_Squares_resized_and_Local_binary_pattern_in_gray_scale_images/links/546f374b0cf24af340c07a96/Vitality-assessment-of-boar-sperm-using-N-Concentric-Squares-resized-and-Local-binary-pattern-in-gray-scale-images.pdf},

year  = {2012},

date = {2012-01-01},

urldate = {2012-01-01},

journal = {XXXIII Jornadas de Automatica},

abstract = {This work introduces a new texture descriptor combining Local Binary Pattern (LBP) and N-concentric squares resized (NCSR) to classify boar spermatozoa as dead or alive using grayscale images. The classifier used was Support Vector Machine (SVM), and the best performance (78.67% hit rate) was achieved with NCSR of 50 features and LBP with 16 neighbors. This result outperforms previous methods in the field, making it a promising tool for automating sperm vitality classification in veterinary practices.},

keywords = {Biomedical Image, local binary pattern, Sperm Vitality, texture classification},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_vitality_2011,

title = {Vitality assessment of boar sperm using N concentric squares resized (NCSR) texture descriptor in digital images},

author = {Enrique Alegre and María Teresa García-Ordás and Victor González-Castro and S Karthikeyan},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21257-4_67},

year  = {2011},

date = {2011-01-01},

journal = {Pattern Recognition and Image Analysis: 5th Iberian Conference, IbPRIA 2011, Las Palmas de Gran Canaria, Spain, June 8-10, 2011. Proceedings 5},

pages = {540–547},

abstract = {Two new textural descriptor, named N Concentric Squares Resized (NCSR) and N Concentric Squares Histogram (NCSH), have been proposed. These descriptors were used to classify 472 images of alive spermatozoa heads and 376 images of dead spermatozoa heads. The results obtained with these two novel descriptors have been compared with a number of classical descriptors such as Haralick, Pattern Spectrum, WSF, Zernike, Flusser and Hu. The feature vectors computed have been classified using kNN and a backpropagation Neural Network. The error rate obtained for NCSR with N = 11 was of 23.20% outperforms the rest of descriptors. Also, the area under the ROC curve (AUC) and the values observed in the ROC curve indicates the performance of the proposed descriptor is better than the others texture description methods.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {local binary pattern, Sperm Vitality, support vector machine, texture descriptors},

pubstate = {published},

tppubtype = {article}

}