Publications

@article{azzopardi_increased_2016,

title = {Increased generalization capability of trainable cosfire filters with application to machine vision},

author = {George Azzopardi and Laura Fernández-Robles and Enrique Alegre and Nicolai Petkov},

url = {https://ieeexplore.ieee.org/abstract/document/7900152},

year  = {2016},

date = {2016-01-01},

journal = {2016 23rd International Conference on Pattern Recognition (ICPR)},

pages = {3356–3361},

abstract = {This paper proposes an optimization for COSFIRE filters using a genetic algorithm to select the best contour parts for pattern recognition. The optimized COSFIRE filters, tested on a dataset of milling machine images, showed improved precision (P = 91.99%) and recall (R = 96.22%) compared to the original version, enhancing efficiency and generalization while maintaining selectivity.},

note = {Publisher: IEEE},

keywords = {COSFIRE Filters, Genetic Algorithm, Image classification, Pattern Recognition},

pubstate = {published},

tppubtype = {article}

}

@article{sanchez-gonzalez_classification_2005,

title = {Classification of boar spermatozoid head images using a model intracellular density distribution},

author = {Lidia Sánchez-González and Nicolai Petkov and Enrique Alegre},

url = {https://link.springer.com/chapter/10.1007/11578079_17},

year  = {2005},

date = {2005-01-01},

journal = {Progress in Pattern Recognition, Image Analysis and Applications: 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Havana, Cuba, November 15-18, 2005. Proceedings 10},

pages = {154–160},

abstract = {A novel method is proposed to classify boar spermatozoid heads based on intracellular intensity distribution. A model distribution is created from normal samples, and deviations are used for classification. The decision criterion minimizes classification errors, achieving a global error of 20.40%.},

note = {Publisher: Springer Berlin Heidelberg},

keywords = {Biomedical Imaging, Image classification, machine learning, Pattern Recognition, Sperm Analysis},

pubstate = {published},

tppubtype = {article}

}