Publications
2012
1.
Olivera, Óscar García-Olalla; García-Ordás, María Teresa; García-Ordás, Diego; Fernández-Robles, Laura; Alegre, Enrique
Vitality assessment of boar sperm using n concentric squares resized and local binary pattern in gray scale images Artículo de revista
En: XXXIII Jornadas de Automatica, 2012.
Resumen | Enlaces | BibTeX | Etiquetas: Biomedical Image, local binary pattern, Sperm Vitality, texture classification
@article{garcia-olalla_vitality_2012,
title = {Vitality assessment of boar sperm using n concentric squares resized and local binary pattern in gray scale images},
author = {Óscar García-Olalla Olivera and María Teresa García-Ordás and Diego García-Ordás and Laura Fernández-Robles and Enrique Alegre},
url = {https://www.researchgate.net/profile/Oscar-Garcia-Olalla/publication/268519830_Vitality_assessment_of_boar_sperm_using_N_Concentric_Squares_resized_and_Local_binary_pattern_in_gray_scale_images/links/546f374b0cf24af340c07a96/Vitality-assessment-of-boar-sperm-using-N-Concentric-Squares-resized-and-Local-binary-pattern-in-gray-scale-images.pdf},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {XXXIII Jornadas de Automatica},
abstract = {This work introduces a new texture descriptor combining Local Binary Pattern (LBP) and N-concentric squares resized (NCSR) to classify boar spermatozoa as dead or alive using grayscale images. The classifier used was Support Vector Machine (SVM), and the best performance (78.67% hit rate) was achieved with NCSR of 50 features and LBP with 16 neighbors. This result outperforms previous methods in the field, making it a promising tool for automating sperm vitality classification in veterinary practices.},
keywords = {Biomedical Image, local binary pattern, Sperm Vitality, texture classification},
pubstate = {published},
tppubtype = {article}
}
This work introduces a new texture descriptor combining Local Binary Pattern (LBP) and N-concentric squares resized (NCSR) to classify boar spermatozoa as dead or alive using grayscale images. The classifier used was Support Vector Machine (SVM), and the best performance (78.67% hit rate) was achieved with NCSR of 50 features and LBP with 16 neighbors. This result outperforms previous methods in the field, making it a promising tool for automating sperm vitality classification in veterinary practices.