Publications

@article{garcia-olalla_tool_2015,

title = {Tool wear classification using LBP-based descriptors combined with LOSIB-based enhancers},

author = {Óscar García-Olalla Olivera and Enrique Alegre and Joaquín Barreiro and Laura Fernández-Robles and María Teresa García-Ordás},

url = {https://www.sciencedirect.com/science/article/pii/S187770581504494X},

year  = {2015},

date = {2015-01-01},

urldate = {2015-01-01},

journal = {Procedia engineering},

volume = {132},

pages = {950–957},

abstract = {This paper presents an automatic tool wear detection method using computer vision and texture recognition. Two LBP-based methods combined with the LOSIB texture booster were evaluated on a dataset of 577 images. Binary (Low-High) and ternary (Low-Medium-High) classifications were performed, achieving 80.58% and 67.76% accuracy, respectively. Results highlight the potential for cost and time savings in industrial tool condition monitoring systems (TCMS).},

note = {Publisher: No longer published by Elsevier},

keywords = {LBP, LOSIB, Monitoring, TCM, texture description, Tool wear},

pubstate = {published},

tppubtype = {article}

}

@article{garcia-olalla_olivera_local_2014,

title = {Local oriented statistics information booster (LOSIB) for texture classification},

author = {Óscar García-Olalla Olivera and Enrique Alegre and Laura Fernández-Robles and Víctor González-Castro},

url = {https://ieeexplore.ieee.org/abstract/document/6976911},

year  = {2014},

date = {2014-01-01},

journal = {2014 22nd international conference on pattern recognition},

pages = {1114–1119},

abstract = {This paper presents the Local Oriented Statistical Information Booster (LOSIB), a descriptor enhancer that extracts gray level differences along multiple orientations. By combining LOSIB with classical descriptors like WCF4 (Wavelet Co-occurrence Features) and LBP (Local Binary Pattern), classification performance is improved on the KTH-Tips-2a and Brodatz32 datasets. Results show improvements of up to 16.94% on KTH and 7.55% on Brodatz using SVM. Additionally, combining LOSIB with CLBP (Complete LBP) also enhances performance on both datasets.},

note = {Publisher: IEEE},

keywords = {LBP, LOSIB, SVM, texture descriptors},

pubstate = {published},

tppubtype = {article}

}