Publications
2012
1.
Morala-Argüello, Patricia; Barreiro, Joaquín; Alegre, Enrique
A evaluation of surface roughness classes by computer vision using wavelet transform in the frequency domain Artículo de revista
En: The International Journal of Advanced Manufacturing Technology, vol. 59, pp. 213–220, 2012, (Publisher: Springer-Verlag).
Resumen | Enlaces | BibTeX | Etiquetas: neural networks, quality inspection, surface roughness, texture analysis, wavelet transform
@article{morala-arguello_evaluation_2012,
title = {A evaluation of surface roughness classes by computer vision using wavelet transform in the frequency domain},
author = {Patricia Morala-Argüello and Joaquín Barreiro and Enrique Alegre},
url = {https://link.springer.com/article/10.1007/s00170-011-3480-6},
year = {2012},
date = {2012-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {59},
pages = {213–220},
abstract = {This study proposes a multiresolution method for unmanned visual quality inspection and surface roughness discrimination in turning. Using wavelet transform, texture features were extracted from surface images, focusing on gray levels in vertical detail sub-images. A multilayer Perceptron neural network classified textures, achieving error rates between 2.59% and 4.17%.},
note = {Publisher: Springer-Verlag},
keywords = {neural networks, quality inspection, surface roughness, texture analysis, wavelet transform},
pubstate = {published},
tppubtype = {article}
}
This study proposes a multiresolution method for unmanned visual quality inspection and surface roughness discrimination in turning. Using wavelet transform, texture features were extracted from surface images, focusing on gray levels in vertical detail sub-images. A multilayer Perceptron neural network classified textures, achieving error rates between 2.59% and 4.17%.
2009
2.
Morala-Argüello, Patricia; Alegre, Enrique; Suarez, S; González-Castro, Víctor; Barreiro, Joaquín
QUALITIVE SURFACE ROUGHNESS EVALUATION USING HARALICK FEATURES AND WAVELET TRANSFORM. Artículo de revista
En: Annals of DAAAM & Proceedings, 2009.
Resumen | Enlaces | BibTeX | Etiquetas: Automated Vision System, Haralick Features, Surface Finish Classification, wavelet transform
@article{morala-arguello_qualitive_2009,
title = {QUALITIVE SURFACE ROUGHNESS EVALUATION USING HARALICK FEATURES AND WAVELET TRANSFORM.},
author = {Patricia Morala-Argüello and Enrique Alegre and S Suarez and Víctor González-Castro and Joaquín Barreiro},
url = {https://openurl.ebsco.com/EPDB%3Agcd%3A10%3A28782154/detailv2?sid=ebsco%3Aplink%3Ascholar-a&id=ebsco%3Agcd%3A47081114&crl=c&jrnl=17269679&id_token_hint=eyJraWQiOiIxNjg2MTQ5MjEzNjMxIiwiYWxnIjoiUlMyNTYifQ.eyJzdWIiOiJzNTY4MTYyNy5tYWluIiwiZGV2aWNlX2lkIjoiNTRmNmMzNGMtZDRmZC00ODk2LThhZmUtYzliMTRmYTA5ZDU3IiwiYW1yIjpbImlwIl0sImlzcyI6Imh0dHBzOlwvXC9hdXRoLmVic2NvLnpvbmVcL2FwaVwvZGlzcGF0Y2hlciIsImNsaWVudF9pZCI6ImF3Z3ljSXg1N01yd25EUTVoNFVlNnlDVkVQMHI1TXQ5IiwiYXVkIjpbImh0dHBzOlwvXC9hdXRoLmVic2NvLnpvbmVcL2FwaVwvZGlzcGF0Y2hlciIsImh0dHA6XC9cL2F1dGgtY3h0LW1nci5jaGV3aWUtbGl2ZS5la3MuZWhvc3QtbGl2ZS5laXNsei5jb20iLCJodHRwczpcL1wvYWNjZXNzLWFwaS5lYnNjby5jb20iLCJhd2d5Y0l4NTdNcnduRFE1aDRVZTZ5Q1ZFUDByNU10OSJdLCJhenAiOiJhd2d5Y0l4NTdNcnduRFE1aDRVZTZ5Q1ZFUDByNU10OSIsImlkdCI6Imluc3RpdHV0aW9uYWwiLCJleHAiOjE3Mzk1Mzg5MzYsImlhdCI6MTczOTUzNTMzNiwiY2xpZW50X25hbWUiOiJXZWJhdXRoIC0gTmV3IEVEUyBpZF90b2tlbiIsImp0aSI6ImYyYzk4MzllLWUzZWYtNDdmMy04OTg0LWRiZmJhZDk2MjA5YyIsInVzZXJuYW1lIjoiSW5zdGl0dXRpb25hbFVzZXIifQ.eZENRNegI61Xbe4u0bWWNLkdMQUU0LhmDpaOpB78k-UCEP_6MoiccAupzgRxrOu8pX28n6styjtAMyQinyrisZEOxtq0ME2Jnv_oJ6x7pGuUWSrwvI-WgQqqWiZ5flbL4jopLZsgmwDHbFDJe6OIa8Gn-HvwgTOYJRHFCzsJj6qegsuQzukkPRRlat_JXK0eMAzD93rITuwfB9SepfezSD1TWKz63m0ynv56MyQYkKXaEWZnmUQESOT6Qk2Mb4IgxuPO9r-6j5ImCDfsqojX99lPE6fT1WpwAGFLh7m2cKXKtVfSP6k6i6vM25Bn7Z8-s3fDxpRtvUCbxX-L4B1fVw&link_origin=scholar.google.es},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
journal = {Annals of DAAAM & Proceedings},
abstract = {This paper discusses the development of an automated vision system for classifying surface finish in turning parts using Haralick features in the frequency domain through wavelet transform. The study compares images of parts with low and high roughness, extracting Haralick features for image description. Classification is carried out using the k-nearest neighbors (KNN) algorithm, and the results are compared with other simpler features based on moments.},
keywords = {Automated Vision System, Haralick Features, Surface Finish Classification, wavelet transform},
pubstate = {published},
tppubtype = {article}
}
This paper discusses the development of an automated vision system for classifying surface finish in turning parts using Haralick features in the frequency domain through wavelet transform. The study compares images of parts with low and high roughness, extracting Haralick features for image description. Classification is carried out using the k-nearest neighbors (KNN) algorithm, and the results are compared with other simpler features based on moments.
0000
3.
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.