Publications
2010
1.
Suárez-Castrillón, Alexci; Alegre, Enrique; Barreiro, Joaquín; Morala-Argüello, Patricia; Fernández-Robles, Laura
Surface roughness classification in metallic parts using Haralick descriptors and quadratic discriminant analysis Artículo de revista
En: Annals of DAAAM & Proceedings, pp. 869–871, 2010, (Publisher: DAAAM International Vienna).
Resumen | Enlaces | BibTeX | Etiquetas: co-occurrence matrix, Discriminant Analysis, Haralick, roughness, surface texture
@article{castrillon_surface_2010,
title = {Surface roughness classification in metallic parts using Haralick descriptors and quadratic discriminant analysis},
author = {Alexci Suárez-Castrillón and Enrique Alegre and Joaquín Barreiro and Patricia Morala-Argüello and Laura Fernández-Robles},
url = {https://go.gale.com/ps/i.do?id=GALE%7CA246014000&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=17269679&p=AONE&sw=w&userGroupName=anon%7Ea266eb37&aty=open-web-entry},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
journal = {Annals of DAAAM & Proceedings},
pages = {869–871},
abstract = {An artificial vision system has been used to classify metallic work-parts in base of their surface roughness. Haralick features have been computed through the gray-level co-occurrence matrix (GLCM) to analyze the texture of the parts. Quadratic and Linear Discriminant Analysis (QDA and LDA) algorithms have been worked out to classify the descriptors. Results have proved the validity of this method to classify metallic parts in two classes achieving hit rates of 97,4% using QDA.},
note = {Publisher: DAAAM International Vienna},
keywords = {co-occurrence matrix, Discriminant Analysis, Haralick, roughness, surface texture},
pubstate = {published},
tppubtype = {article}
}
An artificial vision system has been used to classify metallic work-parts in base of their surface roughness. Haralick features have been computed through the gray-level co-occurrence matrix (GLCM) to analyze the texture of the parts. Quadratic and Linear Discriminant Analysis (QDA and LDA) algorithms have been worked out to classify the descriptors. Results have proved the validity of this method to classify metallic parts in two classes achieving hit rates of 97,4% using QDA.
0000
2.
Suárez, S; Alegre, Enrique; Barreiro, Joaquín; Morala-Arguello, Patricia; González-Castro, Víctor
PDF OFF-PRINTS Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: co-occurrence matrix, laws, roughness, surface texture
@article{suarez_pdf_nodate,
title = {PDF OFF-PRINTS},
author = {S Suárez and Enrique Alegre and Joaquín Barreiro and Patricia Morala-Arguello and Víctor González-Castro},
url = {https://www.researchgate.net/profile/J-Barreiro/publication/235792932_Classification_and_correlation_of_surface_roughness_in_metallic_parts_using_texture_descriptors/links/09e415138dec0b3b71000000/Classification-and-correlation-of-surface-roughness-in-metallic-parts-using-texture-descriptors.pdf},
abstract = {This paper presents a method for classifying surface roughness in machined metallic parts using an artificial vision system. Two texture analysis methods, GLCM and Laws' energy method, are used as descriptors. Classification is performed using LDA, QDA, and ANN, with the best results (94.23%) achieved using Neural Networks. The method successfully correlates texture descriptors with average roughness (Ra).},
keywords = {co-occurrence matrix, laws, roughness, surface texture},
pubstate = {published},
tppubtype = {article}
}
This paper presents a method for classifying surface roughness in machined metallic parts using an artificial vision system. Two texture analysis methods, GLCM and Laws' energy method, are used as descriptors. Classification is performed using LDA, QDA, and ANN, with the best results (94.23%) achieved using Neural Networks. The method successfully correlates texture descriptors with average roughness (Ra).
3.
Morala-Argüello, Patricia; Fernández-Robles, Laura
SURFACE ROUGHNESS CLASSIFICATION IN METALLIC PARTS USING HARALICK DESCRIPTORS AND QUADRATIC DISCRIMINANT ANALYSIS Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: Co-ocurrence Matrix, discriminantAnalysis, Haralick, roughness, surface texture
@article{morala-arguello_surface_nodate,
title = {SURFACE ROUGHNESS CLASSIFICATION IN METALLIC PARTS USING HARALICK DESCRIPTORS AND QUADRATIC DISCRIMINANT ANALYSIS},
author = {Patricia Morala-Argüello and Laura Fernández-Robles},
url = {https://www.daaam.info/Downloads/Pdfs/proceedings/proceedings_2010/23374_Symp_1_head.pdf},
abstract = {An artificial vision system has been used to classify
metallic work-parts in base of their surface roughness.
Haralick features have been computed through the gray-level
co-occurrence matrix (GLCM) to analyze the texture of the
parts. Quadratic and Linear Discriminant Analysis (QDA and
LDA) algorithms have been worked out to classify the
descriptors. Results have proved the validity of this method to
classify metallic parts in two classes achieving hit rates of
97,4% using QDA.},
keywords = {Co-ocurrence Matrix, discriminantAnalysis, Haralick, roughness, surface texture},
pubstate = {published},
tppubtype = {article}
}
An artificial vision system has been used to classify
metallic work-parts in base of their surface roughness.
Haralick features have been computed through the gray-level
co-occurrence matrix (GLCM) to analyze the texture of the
parts. Quadratic and Linear Discriminant Analysis (QDA and
LDA) algorithms have been worked out to classify the
descriptors. Results have proved the validity of this method to
classify metallic parts in two classes achieving hit rates of
97,4% using QDA.
metallic work-parts in base of their surface roughness.
Haralick features have been computed through the gray-level
co-occurrence matrix (GLCM) to analyze the texture of the
parts. Quadratic and Linear Discriminant Analysis (QDA and
LDA) algorithms have been worked out to classify the
descriptors. Results have proved the validity of this method to
classify metallic parts in two classes achieving hit rates of
97,4% using QDA.