Publications

@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}

}

@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}

}