Publications
2010
1.
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.
2008
2.
Alegre, Enrique; Barreiro, Joaquín; Castejón-Limas, Manuel; Suárez, S
Computer vision and classification techniques on the surface finish control in machining processes Artículo de revista
En: International Conference Image Analysis and Recognition, pp. 1101–1110, 2008, (Publisher: Springer Berlin Heidelberg Berlin, Heidelberg).
Resumen | Enlaces | BibTeX | Etiquetas: AISI 303, Computer vision, Product Quality Inspection, Surface Finish Control, texture descriptors
@article{alegre_computer_2008,
title = {Computer vision and classification techniques on the surface finish control in machining processes},
author = {Enrique Alegre and Joaquín Barreiro and Manuel Castejón-Limas and S Suárez},
url = {https://link.springer.com/chapter/10.1007/978-3-540-69812-8_110},
year = {2008},
date = {2008-01-01},
urldate = {2008-01-01},
journal = {International Conference Image Analysis and Recognition},
pages = {1101–1110},
abstract = {This work presents a method for surface finish control using computer vision. The test parts were made of AISI 303 stainless steel and machined with a CNC lathe. Using a Pulnix camera, diffuse illumination, and industrial zoom, 140 images were captured. Three feature extraction methods were applied: histogram statistics, Haralick descriptors, and Laws descriptors. Using k-NN, the best hit rate achieved was 92.14% with unfiltered images using Laws features. These results demonstrate the feasibility of using texture descriptors to assess the roughness of metallic parts for quality inspection.},
note = {Publisher: Springer Berlin Heidelberg Berlin, Heidelberg},
keywords = {AISI 303, Computer vision, Product Quality Inspection, Surface Finish Control, texture descriptors},
pubstate = {published},
tppubtype = {article}
}
This work presents a method for surface finish control using computer vision. The test parts were made of AISI 303 stainless steel and machined with a CNC lathe. Using a Pulnix camera, diffuse illumination, and industrial zoom, 140 images were captured. Three feature extraction methods were applied: histogram statistics, Haralick descriptors, and Laws descriptors. Using k-NN, the best hit rate achieved was 92.14% with unfiltered images using Laws features. These results demonstrate the feasibility of using texture descriptors to assess the roughness of metallic parts for quality inspection.
3.
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.