Publications
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1.
Fernández-Robles, Laura; Olivera, Óscar García-Olalla; García-Ordás, María Teresa; García-Ordás, Diego; Alegre, Enrique
SVM APPROACH TO CLASSIFY BOAR ACROSOME INTEGRITY OF A MULTI-FEATURES SURF Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: Image classification, Image Recognition, Invariant Local Features, support vector machine, SURF
@article{fernandez-robles_svm_nodate,
title = {SVM APPROACH TO CLASSIFY BOAR ACROSOME INTEGRITY OF A MULTI-FEATURES SURF},
author = {Laura Fernández-Robles and Óscar García-Olalla Olivera and María Teresa García-Ordás and Diego García-Ordás and Enrique Alegre},
url = {https://d1wqtxts1xzle7.cloudfront.net/44449818/SVM_approach_to_classify_boar_acrosome_i20160405-28158-qszl8d-libre.pdf?1459894369=&response-content-disposition=inline%3B+filename%3DSVM_Approach_to_Classify_Boar_Acrosome_I.pdf&Expires=1739795517&Signature=Tgnu3YoKmzyQiRloeYT95Z4ufJAMUtL~2z~sVtWh4x0OwtjsDwwxq7cUYjl-q5NxrhAJJNz3b7f7YchGOHb6p7lf48EUqtmL1Cjm1mI6YY59k3-ds8J53mCRa0SdXtjjZa0MvchGa2Aqbqx3pt5Ep6v5To7Trx3aKfElmzjdaSP7yKZxPa~b92YaH02HFDTQkx8UFEf6TuCoitK-mz4On4xw-6-RfHwdh37FtKePaXdKxv~sHwmvwVWlOn~yaNIPTO1sl3X8LT9zuUU~8yHltm8xUlFuOzWXwgAe8bMmYMWr6HwY-GG7ExpJQj43FmIa6XXflt7MlJRIuAzSgSo~Lw__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA},
abstract = {This paper presents an approach to improve the classification of invariant local feature descriptors in images of boar spermatozoa heads using Support Vector Machine (SVM). The method involves detecting interest points with SURF and classifying the acrosome as intact or damaged. The approach focuses on classifying the whole head rather than individual points, leveraging the fact that a head typically has more distinctive points of its own class than doubtful ones. The results show a hit rate of 90.91%, indicating that this method could be an effective alternative for classifying invariant local features.},
keywords = {Image classification, Image Recognition, Invariant Local Features, support vector machine, SURF},
pubstate = {published},
tppubtype = {article}
}
This paper presents an approach to improve the classification of invariant local feature descriptors in images of boar spermatozoa heads using Support Vector Machine (SVM). The method involves detecting interest points with SURF and classifying the acrosome as intact or damaged. The approach focuses on classifying the whole head rather than individual points, leveraging the fact that a head typically has more distinctive points of its own class than doubtful ones. The results show a hit rate of 90.91%, indicating that this method could be an effective alternative for classifying invariant local features.