Publications
2012
1.
Alegre, Enrique; Barreiro, Joaquín; Suárez-Castrillón, Alexci
A new improved Laws-based descriptor for surface roughness evaluation Artículo de revista
En: The International Journal of Advanced Manufacturing Technology, vol. 59, pp. 605–615, 2012, (Publisher: Springer-Verlag).
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, machining processes, surface roughness, texture analysis
@article{alegre_new_2012,
title = {A new improved Laws-based descriptor for surface roughness evaluation},
author = {Enrique Alegre and Joaquín Barreiro and Alexci Suárez-Castrillón},
url = {https://link.springer.com/article/10.1007/s00170-011-3507-z},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {The International Journal of Advanced Manufacturing Technology},
volume = {59},
pages = {605–615},
abstract = {A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes.},
note = {Publisher: Springer-Verlag},
keywords = {Computer vision, machining processes, surface roughness, texture analysis},
pubstate = {published},
tppubtype = {article}
}
A new descriptor that allows to classify turned metallic parts based on their superficial roughness is proposed in this paper. The material used for the tests was AISI 6150 steel, regarded as one of the reference steels in the market. The proposed solution is based on a vision system that calculates the actual roughness by analysing texture on images of machined parts. A new developed R5SR5S kernel for quantifying roughness is based on the R5R5 mask presented by Laws. Results from computing standard deviation from images obtained with the proposed R5SR5S kernel allow us to classify the images with a hit rate of 95.87% using linear discriminant analysis and 97.30% using quadratic discriminant analysis. These results show that the proposed technique can be effectively used to evaluate roughness in machining processes.
2.
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%.
2010
3.
Alegre, Enrique; Alaiz-Rodríguez, Rocío; Barreiro, Joaquín; Fidalgo, Eduardo; Fernández-Robles, Laura
Surface finish control in machining processes using haralick descriptors and neuronal networks Artículo de revista
En: Computational Modeling of Objects Represented in Images: Second International Symposium, CompIMAGE 2010, Buffalo, NY, USA, May 5-7, 2010. Proceedings 2, pp. 231–241, 2010, (Publisher: Springer Berlin Heidelberg).
Resumen | Enlaces | BibTeX | Etiquetas: Classification Methods, Computer vision, Haralick Descriptors, Surface Finish Control, surface roughness
@article{alegre_surface_2010,
title = {Surface finish control in machining processes using haralick descriptors and neuronal networks},
author = {Enrique Alegre and Rocío Alaiz-Rodríguez and Joaquín Barreiro and Eduardo Fidalgo and Laura Fernández-Robles},
url = {https://link.springer.com/chapter/10.1007/978-3-642-12712-0_21},
year = {2010},
date = {2010-01-01},
journal = {Computational Modeling of Objects Represented in Images: Second International Symposium, CompIMAGE 2010, Buffalo, NY, USA, May 5-7, 2010. Proceedings 2},
pages = {231–241},
abstract = {This paper presents a computer vision-based method to control surface roughness in steel parts. It classifies steel surfaces into acceptable and defective classes based on roughness. The study uses 143 images of AISI 303 stainless steel and three image description methods: texture local filters, Haralick descriptors, and wavelet transform features. The best error rate of 4.0% was achieved using texture descriptors with K-NN, while the optimal configuration with a neural network achieved a 0.0% error rate using Haralick descriptors.},
note = {Publisher: Springer Berlin Heidelberg},
keywords = {Classification Methods, Computer vision, Haralick Descriptors, Surface Finish Control, surface roughness},
pubstate = {published},
tppubtype = {article}
}
This paper presents a computer vision-based method to control surface roughness in steel parts. It classifies steel surfaces into acceptable and defective classes based on roughness. The study uses 143 images of AISI 303 stainless steel and three image description methods: texture local filters, Haralick descriptors, and wavelet transform features. The best error rate of 4.0% was achieved using texture descriptors with K-NN, while the optimal configuration with a neural network achieved a 0.0% error rate using Haralick descriptors.
2009
4.
Morala-Argüello, Patricia; Barreiro, Joaquín; Alegre, Enrique; González-Castro, Víctor
Application of textural descriptors for the evaluation of surface roughness class in the machining of metals Artículo de revista
En: 2009.
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, machine learning, machining, quality control, surface roughness
@article{morala-arguello_application_2009,
title = {Application of textural descriptors for the evaluation of surface roughness class in the machining of metals},
author = {Patricia Morala-Argüello and Joaquín Barreiro and Enrique Alegre and Víctor González-Castro},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=opCbArQAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=opCbArQAAAAJ:UebtZRa9Y70C},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
abstract = {Surface roughness measurement has been a key topic in metal machining research for decades. Traditional methods rely on tactile devices providing 2D profiles, but advances in computer vision now enable 3D surface characterization. This paper proposes a computer vision-based method to evaluate machined part quality using five feature vectors: Hu, Flusser, Taubin, Zernike, and Legendre moments. Images were classified into low and high roughness using k-NN and neural networks. Results show that Zernike and Legendre descriptors perform best, achieving a 6.5% error rate with k-NN classification.},
keywords = {Computer vision, machine learning, machining, quality control, surface roughness},
pubstate = {published},
tppubtype = {article}
}
Surface roughness measurement has been a key topic in metal machining research for decades. Traditional methods rely on tactile devices providing 2D profiles, but advances in computer vision now enable 3D surface characterization. This paper proposes a computer vision-based method to evaluate machined part quality using five feature vectors: Hu, Flusser, Taubin, Zernike, and Legendre moments. Images were classified into low and high roughness using k-NN and neural networks. Results show that Zernike and Legendre descriptors perform best, achieving a 6.5% error rate with k-NN classification.
2008
5.
Barreiro, Joaquín; Alaiz-Rodríguez, Rocío; Alegre, Enrique; Ablanedo, D
Surface finish control in machining processes using textural descriptors based on moments Miscelánea
2008.
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, Haralick Descriptors, neural networks, Surface Finish Control, surface roughness
@misc{barreiro_surface_2008,
title = {Surface finish control in machining processes using textural descriptors based on moments},
author = {Joaquín Barreiro and Rocío Alaiz-Rodríguez and Enrique Alegre and D Ablanedo},
url = {https://link.springer.com/chapter/10.1007/978-3-642-12712-0_21},
year = {2008},
date = {2008-01-01},
publisher = {na},
abstract = {This paper introduces a computer vision method for controlling the surface finish of steel parts by classifying them into acceptable and defective categories based on surface roughness. The study uses 143 images of AISI 303 stainless steel, described with three techniques: texture local filters, Haralick descriptors, and wavelet transform features. The classification is done with K-NN and neural networks. The best result, with a 4.0% error rate, was achieved using texture descriptors with K-NN. The optimal configuration with a neural network, using Haralick descriptors, resulted in a 0.0% error rate.},
keywords = {Computer vision, Haralick Descriptors, neural networks, Surface Finish Control, surface roughness},
pubstate = {published},
tppubtype = {misc}
}
This paper introduces a computer vision method for controlling the surface finish of steel parts by classifying them into acceptable and defective categories based on surface roughness. The study uses 143 images of AISI 303 stainless steel, described with three techniques: texture local filters, Haralick descriptors, and wavelet transform features. The classification is done with K-NN and neural networks. The best result, with a 4.0% error rate, was achieved using texture descriptors with K-NN. The optimal configuration with a neural network, using Haralick descriptors, resulted in a 0.0% error rate.