Publications
2011
1.
Robles, L Fernández; González-Castro, V; Garcıa-Olalla, O; Garcıa-Ordás, MT; Alegre, E
A local invariant features approach for classifying acrosome integrity in boar spermatozoa Artículo de revista
En: Computational Vision and Medical Image Processing: VipIMAGE 2011, pp. 199, 2011, (Publisher: CRC Press).
Resumen | Enlaces | BibTeX | Etiquetas: acrosome state, sperm cell analysis, SURF method, texture descriptors, veterinary applications
@article{robles_local_2011,
title = {A local invariant features approach for classifying acrosome integrity in boar spermatozoa},
author = {L Fernández Robles and V González-Castro and O Garcıa-Olalla and MT Garcıa-Ordás and E Alegre},
url = {https://books.google.es/books?hl=en&lr=&id=rr7LBQAAQBAJ&oi=fnd&pg=PA199&dq=info:qN1Kvkc9MngJ:scholar.google.com&ots=wusFRCDzZe&sig=uD2_yECMO1Ldc5iYS0gzYkLkGp8&redir_esc=y#v=onepage&q&f=false},
year = {2011},
date = {2011-01-01},
journal = {Computational Vision and Medical Image Processing: VipIMAGE 2011},
pages = {199},
abstract = {In this work we have used a number of texture descriptors to characterize the acrosome state of boar sperm cells, which is a key factor in semen quality control applications. Laws masks, Legendre and Zernike moments, Haralick features extracted from the original image and from the coefficients of the Discrete Wavelet Transform, and descriptors based on interest points using the Speeded-Up Robust Features (SURF) method have been evaluated. Classification using kNN show that the best results were obtained by SURF, with an overall hit rate of 94.88% and, what is more important, a higher hit rate in the damaged (96.86%) than in the intact class (92.89%). These results make this descriptor very attractive for the veterinary community.},
note = {Publisher: CRC Press},
keywords = {acrosome state, sperm cell analysis, SURF method, texture descriptors, veterinary applications},
pubstate = {published},
tppubtype = {article}
}
In this work we have used a number of texture descriptors to characterize the acrosome state of boar sperm cells, which is a key factor in semen quality control applications. Laws masks, Legendre and Zernike moments, Haralick features extracted from the original image and from the coefficients of the Discrete Wavelet Transform, and descriptors based on interest points using the Speeded-Up Robust Features (SURF) method have been evaluated. Classification using kNN show that the best results were obtained by SURF, with an overall hit rate of 94.88% and, what is more important, a higher hit rate in the damaged (96.86%) than in the intact class (92.89%). These results make this descriptor very attractive for the veterinary community.
2.
Robles, Laura Fernández; Castro, Víctor González; Olalla, O Garcıa; Ordás, María Teresa Garcıa; Alegre, Enrique
A local invariant features approach for classifying acrosome integrity in boar spermatozoa Artículo de revista
En: Computational Vision and Medical Image Processing: VipIMAGE 2011, pp. 199, 2011, (Publisher: CRC Press).
Resumen | Enlaces | BibTeX | Etiquetas: acrosome state, sperm cell analysis, SURF method, texture descriptors, veterinary applications
@article{fernandez_robles_local_2011,
title = {A local invariant features approach for classifying acrosome integrity in boar spermatozoa},
author = {Laura Fernández Robles and Víctor González Castro and O Garcıa Olalla and María Teresa Garcıa Ordás and Enrique Alegre},
url = {https://books.google.es/books?hl=en&lr=&id=rr7LBQAAQBAJ&oi=fnd&pg=PA199&dq=info:qN1Kvkc9MngJ:scholar.google.com&ots=wusFRCDzZe&sig=uD2_yECMO1Ldc5iYS0gzYkLkGp8&redir_esc=y#v=onepage&q&f=false},
year = {2011},
date = {2011-01-01},
journal = {Computational Vision and Medical Image Processing: VipIMAGE 2011},
pages = {199},
abstract = {In this work we have used a number of texture descriptors to characterize the acrosome state of boar sperm cells, which is a key factor in semen quality control applications. Laws masks, Legendre and Zernike moments, Haralick features extracted from the original image and from the coefficients of the Discrete Wavelet Transform, and descriptors based on interest points using the Speeded-Up Robust Features (SURF) method have been evaluated. Classification using kNN show that the best results were obtained by SURF, with an overall hit rate of 94.88% and, what is more important, a higher hit rate in the damaged (96.86%) than in the intact class (92.89%). These results make this descriptor very attractive for the veterinary community.},
note = {Publisher: CRC Press},
keywords = {acrosome state, sperm cell analysis, SURF method, texture descriptors, veterinary applications},
pubstate = {published},
tppubtype = {article}
}
In this work we have used a number of texture descriptors to characterize the acrosome state of boar sperm cells, which is a key factor in semen quality control applications. Laws masks, Legendre and Zernike moments, Haralick features extracted from the original image and from the coefficients of the Discrete Wavelet Transform, and descriptors based on interest points using the Speeded-Up Robust Features (SURF) method have been evaluated. Classification using kNN show that the best results were obtained by SURF, with an overall hit rate of 94.88% and, what is more important, a higher hit rate in the damaged (96.86%) than in the intact class (92.89%). These results make this descriptor very attractive for the veterinary community.