Publications

@article{mazo_ontology-based_2018,

title = {Ontology-based automatic reclassification of tissues and organs in histological images},

author = {Claudia Mazo and María Trujillo and Enrique Alegre and Liliana Salazar},

url = {https://ceur-ws.org/Vol-2100/paper9.pdf},

year  = {2018},

date = {2018-01-01},

journal = {Proceedings of the 12th Alberto Mendelzon International Workshop on Foundations of},

volume = {390},

pages = {1–4},

abstract = {Heterogeneous data sources, such as images and human knowledge, require different processing approaches. This paper integrates visual descriptors and an ontology to classify tissues and organs of the human cardiovascular system. A cascade Support Vector Machine (SVM) first classifies images based on texture descriptors, and then results are refined using a histological ontology. This combined approach improves classification accuracy compared to image-based methods alone.},

keywords = {Automatic Classification, histological ontology, Histology Images, image processing},

pubstate = {published},

tppubtype = {article}

}

@article{chaves_systematic_2018,

title = {A systematic review on object localisation methods in images},

author = {Deisy Chaves and Surajit Saikia and Laura Fernández-Robles and Enrique Alegre and María Trujillo},

url = {https://polipapers.upv.es/index.php/RIAI/article/view/10229},

year  = {2018},

date = {2018-01-01},

journal = {Revista Iberoamericana de Automática e Informática Industrial},

volume = {15},

number = {3},

pages = {231–242},

abstract = {This article provides a systematic review of methods for precise object localization in images, covering techniques from traditional sliding window methods (e.g., Viola-Jones) to modern deep learning-based approaches like Faster-RCNN and Mask-RCNN. It discusses the advantages, disadvantages, and applications of these methods in fields such as industrial inspection, clinical diagnosis, and obstacle detection in vehicles and robots. The review offers an organized summary of these techniques and highlights future research directions.},

note = {Publisher: UNIV POLITECNICA VALENCIA, EDITORIAL UPV CAMINO VERA SN, VALENCIA, 46022, SPAIN},

keywords = {automated detection, Computer vision, deep learning, Faster-RCCN, image processing, Mask-RCNN, object localization, visual inspection},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_classification_2017,

title = {Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM},

author = {Claudia Mazo and Enrique Alegre and María Trujillo},

url = {https://www.sciencedirect.com/science/article/pii/S0169260716305910},

year  = {2017},

date = {2017-01-01},

journal = {Computer methods and programs in biomedicine},

volume = {147},

pages = {1–10},

abstract = {Histological images have characteristics, such as texture, shape, colour and spatial structure, that permit the differentiation of each fundamental tissue and organ. Texture is one of the most discriminative features. The automatic classification of tissues and organs based on histology images is an open problem, due to the lack of automatic solutions when treating tissues without pathologies.},

note = {Publisher: Elsevier},

keywords = {Automatic Classification, Fundamental Tissues, Histology Images, image processing, Organs of the Cardiovascular System},

pubstate = {published},

tppubtype = {article}

}

@article{mazo_tissues_2017,

title = {Tissues classification of the cardiovascular system using texture descriptors},

author = {Claudia Mazo and Enrique Alegre and Maria Trujillo and Víctor González-Castro},

url = {https://link.springer.com/chapter/10.1007/978-3-319-60964-5_11},

year  = {2017},

date = {2017-01-01},

journal = {Annual Conference on Medical Image Understanding and Analysis},

pages = {123–132},

abstract = {This paper presents an automated classification approach for cardiovascular tissues using texture analysis. Rotation-invariant Local Binary Patterns (LBPri) and Haralick features were evaluated as descriptors, while Random Forest (RF) and Linear Discriminant Analysis (LDA) were tested for classification. The study categorized tissues into four classes, achieving high AUC values (up to 0.9994) using LBPri and RF, demonstrating the effectiveness of this method for tissue identification.},

note = {Publisher: Springer International Publishing Cham},

keywords = {Automatic Classification, Fundamental Tissues, Histology Images, image processing, Organs of the Cardiovascular System},

pubstate = {published},

tppubtype = {article}

}

@article{fernandez-robles_object_2016,

title = {Object recognition techniques in real applications},

author = {Laura Fernández-Robles},

url = {https://research.rug.nl/en/publications/object-recognition-techniques-in-real-applications},

year  = {2016},

date = {2016-01-01},

abstract = {This doctoral thesis presents object description and retrieval techniques applied to three different fields: boar spermatozoa classification based on acrosome integrity, tool wear monitoring in machining processes, and specific object detection in images to combat child sexual exploitation. The research develops new methods and descriptors, highlighting the creation of the colour COSFIRE filter, which enhances color description and object discrimination while maintaining background invariance.},

keywords = {Computer vision, image processing, machine learning, object recognition},

pubstate = {published},

tppubtype = {article}

}

@article{garcia_ordas_azibo_2015,

title = {aZIBO shape descriptor for monitoring tool wear in milling},

author = {María Teresa García-Ordás and Enrique Alegre and Víctor González-Castro and Óscar García-Olalla Olivera and Joaquín Barreiro and Ana Isabel Fernández-Abia},

url = {https://www.sciencedirect.com/science/article/pii/S1877705815044951},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Procedia Engineering},

volume = {132},

pages = {958–965},

abstract = {This paper proposes an automated method for estimating insert wear in metal machining to optimize tool replacement. The aZIBO shape descriptor (absolute Zernike moments with Invariant Boundary Orientation) is used for wear characterization. A dataset of 577 wear regions was classified into two (Low-High) and three (Low-Medium-High) classes using kNN and SVM classifiers. aZIBO outperformed traditional shape descriptors, achieving success rates of 91.33% for two-class and 90.12% for three-class classification.},

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

keywords = {image processing, machine learning, metal machining, shape descriptors, tool wear detection},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_color_2014,

title = {Color Adaptive Neighborhood Mathematical Morphology and its application to pixel-level classification},

author = {Víctor González-Castro and Johan Debayle and Jean-Charles Pinoli},

url = {https://www.sciencedirect.com/science/article/pii/S016786551400021X},

year  = {2014},

date = {2014-01-01},

journal = {Pattern Recognition Letters},

volume = {47},

pages = {50–62},

abstract = {This paper explores spatially adaptive Mathematical Morphology (MM) for color images by generalizing the General Adaptive Neighborhood Image Processing (GANIP) approach. It introduces Color Adaptive Neighborhoods (CAN) as adaptive structuring elements (ASE) for morphological operations. The method, applied to various color spaces, outperforms other ASEs in preserving object borders and color transitions. The adaptive morphological operators are further applied to classify color texture images.},

note = {Publisher: North-Holland},

keywords = {Adaptive Structuring Elements, Color Images, image processing, mathematical morphology, texture classification},

pubstate = {published},

tppubtype = {article}

}

@article{gonzalez-castro_adaptive_2014,

title = {Adaptive texture description and estimation of the class prior probabilities for seminal quality control},

author = {Víctor González-Castro},

url = {https://www.raco.cat/index.php/ELCVIA/article/view/281622},

year  = {2014},

date = {2014-01-01},

journal = {ELCVIA: electronic letters on computer vision and image analysis},

volume = {13},

number = {2},

pages = {19–21},

abstract = {Semen quality assessment is essential in artificial insemination for both humans and animals. In livestock farming, high-quality semen samples are crucial for successful fertilization, requiring strict quality control. Currently, sperm vitality and acrosome integrity are assessed manually, which is costly and prone to human errors. This research proposes an automated system based on image processing and machine learning to estimate the proportion of dead spermatozoa and damaged acrosomes using an affordable phase contrast microscope. New intelligent segmentation techniques and adaptive texture description methods have been developed and evaluated to improve automatic boar semen quality estimation.},

keywords = {artificial insemination, image processing, machine learning, semen quality},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_olivera_adaptive_2011,

title = {Adaptive filters evaluation for sharpness enhancement and noise removal},

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

url = {https://www.researchgate.net/profile/Oscar-Garcia-Olalla/publication/229828217_Adaptive_filters_evaluation_for_sharpness_enhancement_and_noise_removal/links/544a1e150cf2ea6541343976/Adaptive-filters-evaluation-for-sharpness-enhancement-and-noise-removal.pdf},

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 = {55},

abstract = {In this work, two adaptive filter algorithms for sharpness enhancement and noise removal have been evaluated. A modification to the Adaptive Bilateral Filter (ABF) method have been carried out using the difference of gaussians as the pixel classification algorithm. A metric comparison with adaptive Non lineal Complex Diffusion Filter (NCDF) algorithm has been applied using three methods: MSE (mean square error), ENL (equivalent number of looks) and CNR (Contrast to noise ratio). Results showed that the proposed modification outperforms all the others using the MSE, CNR and ENL (in light areas) criteria. However, the Adaptive NCDF obtains the best result in the dark areas of the image.},

note = {Publisher: Universidad de Sevilla},

keywords = {adaptive bilateral filter, CNR, ENL, image metrics, image processing, image quality comparison, MSE, noise removal, non-linear filters, pixel classification, sharpness enhancement},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{sanchez-gonzalez_statistical_2005,

title = {Statistical approach to boar semen head classification based on intracellular intensity distribution},

author = {Lidia Sánchez-González and Nicolai Petkov and Enrique Alegre},

url = {https://link.springer.com/chapter/10.1007/11556121_12},

year  = {2005},

date = {2005-01-01},

journal = {International Conference on Computer Analysis of Images and Patterns},

pages = {88–95},

abstract = {This technique estimates the fraction of boar sperm heads exhibiting a normal intracellular density pattern, as defined by veterinary experts. It offers a potential alternative to costly staining methods for sperm capacitation. The method involves training a model using images classified as normal, similar to normal, and abnormal. The deviation of each sperm head from the model is used to calculate a conditional probability, allowing for accurate estimation of normal sperm fractions, with an error below 0.25 in 89% of the test samples.},

note = {Publisher: Springer Berlin Heidelberg Berlin, Heidelberg},

keywords = {Digital Estimation, image processing, Sperm Capacitation, sperm classification, Veterinary Analysis},

pubstate = {published},

tppubtype = {article}

}

@article{alegre_tool_2005,

title = {Tool insert wear classification using statistical descriptors and neuronal networks},

author = {Enrique Alegre and Rocío Aláiz-Rodríguez and Joaquín Barreiro and M Viñuela},

url = {https://link.springer.com/chapter/10.1007/11578079_82},

year  = {2005},

date = {2005-01-01},

journal = {Progress in Pattern Recognition, Image Analysis and Applications: 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005. Proceedings 10},

pages = {786–793},

abstract = {This work proposes an automated method to determine tool insert wear levels using image analysis. Images of tungsten carbide inserts were acquired during machining of AISI SAE 1045 and 4140 steel bars. After pre-processing and wear area segmentation, statistical moment-based descriptors were extracted. Two classification experiments (binary and three-class) were conducted using Lp2, k-NN, and neural networks. Zernike and Legendre descriptors achieved the best results with a multilayer perceptron (MLP) neural network.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {image processing, machine vision, Manufacturing, neural networks, Tool wear},

pubstate = {published},

tppubtype = {article}

}