Publications
2019
1.
Olivera, Óscar García-Olalla; Fernández-Robles, Laura; Alegre, Enrique; Castejón-Limas, Manuel; Fidalgo, Eduardo
Boosting texture-based classification by describing statistical information of gray-levels differences Artículo de revista
En: Sensors, vol. 19, no 5, pp. 1048, 2019, (Publisher: MDPI).
Resumen | Enlaces | BibTeX | Etiquetas: CLOSIB, Local Binary Patterns, Statistica Information of Gray-Levels Differences, texture classification, texture description, Visual Sensors
@article{garcia-olalla_olivera_boosting_2019,
title = {Boosting texture-based classification by describing statistical information of gray-levels differences},
author = {Óscar García-Olalla Olivera and Laura Fernández-Robles and Enrique Alegre and Manuel Castejón-Limas and Eduardo Fidalgo},
url = {https://www.mdpi.com/1424-8220/19/5/1048},
year = {2019},
date = {2019-01-01},
journal = {Sensors},
volume = {19},
number = {5},
pages = {1048},
abstract = {This paper introduces a new texture descriptor booster, CLOSIB (Complete Local Oriented Statistical Information Booster), designed to enhance the discriminative power of texture descriptors like LBP. By using statistical information from image gray-level differences, the method improves texture classification. Variants such as Half-CLOSIB (H-CLOSIB) and Multi-scale CLOSIB (M-CLOSIB) offer increased efficiency and robustness. The method was tested on datasets like KTH TIPS, UIUC, USPTex, and JAFFE, showing improved classification accuracy when combined with LBP-based descriptors. Comparisons with recent algorithms show that CLOSIB variants provide competitive results.},
note = {Publisher: MDPI},
keywords = {CLOSIB, Local Binary Patterns, Statistica Information of Gray-Levels Differences, texture classification, texture description, Visual Sensors},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a new texture descriptor booster, CLOSIB (Complete Local Oriented Statistical Information Booster), designed to enhance the discriminative power of texture descriptors like LBP. By using statistical information from image gray-level differences, the method improves texture classification. Variants such as Half-CLOSIB (H-CLOSIB) and Multi-scale CLOSIB (M-CLOSIB) offer increased efficiency and robustness. The method was tested on datasets like KTH TIPS, UIUC, USPTex, and JAFFE, showing improved classification accuracy when combined with LBP-based descriptors. Comparisons with recent algorithms show that CLOSIB variants provide competitive results.
2018
2.
García-Ordás, María Teresa; Alegre, Enrique; Alaiz-Rodríguez, Rocío; González-Castro, Víctor
Tool wear monitoring using an online, automatic and low cost system based on local texture Artículo de revista
En: Mechanical systems and signal processing, vol. 112, pp. 98–112, 2018, (Publisher: Academic Press).
Resumen | Enlaces | BibTeX | Etiquetas: Patches, texture description, Tool wear, Wear Region
@article{garcia-ordas_tool_2018,
title = {Tool wear monitoring using an online, automatic and low cost system based on local texture},
author = {María Teresa García-Ordás and Enrique Alegre and Rocío Alaiz-Rodríguez and Víctor González-Castro},
url = {https://www.sciencedirect.com/science/article/pii/S088832701830236X},
year = {2018},
date = {2018-01-01},
journal = {Mechanical systems and signal processing},
volume = {112},
pages = {98–112},
abstract = {This work presents a new, cost-effective, and fast approach for determining whether cutting tools in edge profile milling processes are serviceable or disposable, based on wear levels. A new dataset of 254 images of edge profile cutting heads was created, with 577 images of segmented cutting edges, classified as either functional (301) or disposable (276). The proposed method involves dividing the cutting edge into regions (Wear Patches, WP), characterizing them using texture descriptors based on Local Binary Patterns (LBP), and using a Support Vector Machine (SVM) with an intersection kernel to classify the patches. The best configuration achieved an accuracy of 90.26% in detecting disposable cutting edges, showing great potential for automatic wear monitoring in milling.},
note = {Publisher: Academic Press},
keywords = {Patches, texture description, Tool wear, Wear Region},
pubstate = {published},
tppubtype = {article}
}
This work presents a new, cost-effective, and fast approach for determining whether cutting tools in edge profile milling processes are serviceable or disposable, based on wear levels. A new dataset of 254 images of edge profile cutting heads was created, with 577 images of segmented cutting edges, classified as either functional (301) or disposable (276). The proposed method involves dividing the cutting edge into regions (Wear Patches, WP), characterizing them using texture descriptors based on Local Binary Patterns (LBP), and using a Support Vector Machine (SVM) with an intersection kernel to classify the patches. The best configuration achieved an accuracy of 90.26% in detecting disposable cutting edges, showing great potential for automatic wear monitoring in milling.
3.
Olivera, Óscar García-Olalla; Alegre, Enrique; Fernández-Robles, Laura; Fidalgo, Eduardo; Saikia, Surajit
Textile retrieval based on image content from cdc and webcam cameras in indoor environments Artículo de revista
En: Sensors, vol. 18, no 5, pp. 1329, 2018, (Publisher: MDPI).
Resumen | Enlaces | BibTeX | Etiquetas: Content-based Image Retrieval, Textile Localization, Textile Retrieval, texture description, Texture Retrieval, Visual Sensors
@article{garcia-olalla_olivera_textile_2018,
title = {Textile retrieval based on image content from cdc and webcam cameras in indoor environments},
author = {Óscar García-Olalla Olivera and Enrique Alegre and Laura Fernández-Robles and Eduardo Fidalgo and Surajit Saikia},
url = {https://www.mdpi.com/1424-8220/18/5/1329},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Sensors},
volume = {18},
number = {5},
pages = {1329},
abstract = {This paper presents a method for textile-based image retrieval, useful for law enforcement to identify evidence by matching textiles in real-world scenes. The approach uses MSER on high-pass filtered RGB, HSV, and Hue channels to extract textile regions. HOG and HCLOSIB are combined for feature description and correlation distance to match the query textile patch with candidate regions. A new dataset, TextilTube, consisting of 1913 labeled textile regions in 67 classes, is introduced. Experimental results show 84.94% success in the top 40 matches and 37.44% precision for the first match, outperforming current deep learning methods.},
note = {Publisher: MDPI},
keywords = {Content-based Image Retrieval, Textile Localization, Textile Retrieval, texture description, Texture Retrieval, Visual Sensors},
pubstate = {published},
tppubtype = {article}
}
This paper presents a method for textile-based image retrieval, useful for law enforcement to identify evidence by matching textiles in real-world scenes. The approach uses MSER on high-pass filtered RGB, HSV, and Hue channels to extract textile regions. HOG and HCLOSIB are combined for feature description and correlation distance to match the query textile patch with candidate regions. A new dataset, TextilTube, consisting of 1913 labeled textile regions in 67 classes, is introduced. Experimental results show 84.94% success in the top 40 matches and 37.44% precision for the first match, outperforming current deep learning methods.
2015
4.
Olivera, Óscar García-Olalla; Alegre, Enrique; Barreiro, Joaquín; Fernández-Robles, Laura; García-Ordás, María Teresa
Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers Artículo de revista
En: Procedia engineering, vol. 132, pp. 950–957, 2015, (Publisher: No longer published by Elsevier).
Resumen | Enlaces | BibTeX | Etiquetas: LBP, LOSIB, Monitoring, TCM, texture description, Tool wear
@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}
}
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).
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.
González-Castro, Víctor; Alegre, Enrique; Suárez-Castrillón, Alexci; Olivera, Óscar García-Olalla; García-Ordás, María Teresa
Adaptive texture description for semen vitality assessment Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: boar semen, granulometry, pattern spectrum, texture description
@article{gonzalez-castro_adaptive_nodate,
title = {Adaptive texture description for semen vitality assessment},
author = {Víctor González-Castro and Enrique Alegre and Alexci Suárez-Castrillón and Óscar García-Olalla Olivera and María Teresa García-Ordás},
url = {https://d1wqtxts1xzle7.cloudfront.net/47501584/Adaptive_texture_description_for_semen_v20160725-8795-17ysl7b-libre.pdf?1469454941=&response-content-disposition=inline%3B+filename%3DAdaptive_texture_description_for_semen_v.pdf&Expires=1739193493&Signature=SPojfSyGRHX-PAT8i5lgMywJ52650XScp8t74YrAAbRnnZ0mPJbvVLYRGRVG3eNTcH0gHbWwIshfAz3ok9lb8gwRdrSSDSazEnRN5cYx8eBGm1rhAQ0WmI7Cwc0TaLMt1nk41LMRM4hROUQEF9P6YZShfswvVnj~e9IXqGrjksaOYlzuk7Y2zsIzg4jnkgk~w1gJeZ-gxUj~5mdtEESX7zDTdTrbyvs4Roiyoig8~jgMdtkCptWrABzqnovahSK9D-vjedWjo-EVaVue22Wa-k1z7hSSerV1X~AZt68BQqa2GqhwOxQ~qfJdpU4j8o88~hi6RcbTtsGj7-BE9vyWZA__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
abstract = {In this paper an adaptive texture description method based on granulometry
is proposed. It is a variant of the Pattern Spectrum, and both the adaptive
and the ordinary descriptors are used to classify boar spermatozoa into
dead or alive. A set of 845 boar spermatozoon heads were assessed and a
back-propagation neural network was used in order to classify them. We
have described both the original grey level images and the same images
after applying a range texture filter on them. The best hit rates have been
obtained when the adaptive Pattern Spectrum was used to describe the
filtered images.},
keywords = {boar semen, granulometry, pattern spectrum, texture description},
pubstate = {published},
tppubtype = {article}
}
In this paper an adaptive texture description method based on granulometry
is proposed. It is a variant of the Pattern Spectrum, and both the adaptive
and the ordinary descriptors are used to classify boar spermatozoa into
dead or alive. A set of 845 boar spermatozoon heads were assessed and a
back-propagation neural network was used in order to classify them. We
have described both the original grey level images and the same images
after applying a range texture filter on them. The best hit rates have been
obtained when the adaptive Pattern Spectrum was used to describe the
filtered images.
is proposed. It is a variant of the Pattern Spectrum, and both the adaptive
and the ordinary descriptors are used to classify boar spermatozoa into
dead or alive. A set of 845 boar spermatozoon heads were assessed and a
back-propagation neural network was used in order to classify them. We
have described both the original grey level images and the same images
after applying a range texture filter on them. The best hit rates have been
obtained when the adaptive Pattern Spectrum was used to describe the
filtered images.
7.
Alegre, Enrique; González, Maribel; González-Castro, Víctor; Alonso, Tomás; García-Ordás, María Teresa; Olivera, Óscar García-Olalla
Evaluation of mother wavelet functions with statistical texture descriptors to classify boar sperm acrosomes Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: boar semen, texture description, wavelet transform
@article{alegre_evaluation_nodate,
title = {Evaluation of mother wavelet functions with statistical texture descriptors to classify boar sperm acrosomes},
author = {Enrique Alegre and Maribel González and Víctor González-Castro and Tomás Alonso and María Teresa García-Ordás and Óscar García-Olalla Olivera},
url = {https://www.researchgate.net/profile/Oscar-Garcia-Olalla/publication/268519913_Evaluation_of_mother_wavelet_functions_with_statistical_texture_descriptors_to_classify_boar_sperm_acrosomes/links/546f3ce10cf2d67fc031030e/Evaluation-of-mother-wavelet-functions-with-statistical-texture-descriptors-to-classify-boar-sperm-acrosomes.pdf},
abstract = {This study utilizes a backpropagation neural network to determine the acrosome state of boar spermatozoa, evaluating the impact of various wavelet families on the classification accuracy. Five wavelet families—Dauchebies, Coiflets, Symlets, Meyer, and biorthogonal—were applied to the images, from which first and second-order texture descriptors were extracted. The classification process used a neural network, and different configurations were assessed. Results show a 7% variation in hit rates, with the best performance achieved using the biorthogonal and Symlets wavelet families, reaching a 95% success rate.},
keywords = {boar semen, texture description, wavelet transform},
pubstate = {published},
tppubtype = {article}
}
This study utilizes a backpropagation neural network to determine the acrosome state of boar spermatozoa, evaluating the impact of various wavelet families on the classification accuracy. Five wavelet families—Dauchebies, Coiflets, Symlets, Meyer, and biorthogonal—were applied to the images, from which first and second-order texture descriptors were extracted. The classification process used a neural network, and different configurations were assessed. Results show a 7% variation in hit rates, with the best performance achieved using the biorthogonal and Symlets wavelet families, reaching a 95% success rate.