Publications

@article{sanchez-gonzalez_local_2020,

title = {Local binary pattern features to detect anomalies in machined workpiece},

author = {Lidia Sánchez-González and Virginia Riego and Manuel Castejón-Limas and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-030-61705-9_55},

year  = {2020},

date = {2020-01-01},

journal = {International Conference on Hybrid Artificial Intelligence Systems},

pages = {665–673},

abstract = {This paper proposes a vision-based system for evaluating the surface finish of machined workpieces by using Local Binary Pattern (LBP) vectors to represent image textures. The system detects wear on surfaces by analyzing the texture descriptors, as regular patterns correspond to unworn surfaces. Four classification techniques are tested, with the Random Forest algorithm achieving the highest accuracy of 86.0%, meeting the expert requirements for quality control.},

note = {Publisher: Springer International Publishing Cham},

keywords = {local binary pattern, Random Forest Classification, Surface Finish, Wear Detection},

pubstate = {published},

tppubtype = {article}

}