Publications
2017
1.
de Paz Centeno, Iván; Fidalgo, Eduardo; Alegre, Enrique; Al-Nabki, Mhd Wesam
Oculus-Crawl, a software tool for building datasets for computer vision tasks Artículo de revista
En: XXXVIII Jornadas de Automática, pp. 991–998, 2017, (Publisher: Servicio de Publicaciones de la Universidad de Oviedo).
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, crawler, Dataset, images, search engine
@article{paz_centeno_oculus-crawl_2017,
title = {Oculus-Crawl, a software tool for building datasets for computer vision tasks},
author = {Iván de Paz Centeno and Eduardo Fidalgo and Enrique Alegre and Mhd Wesam Al-Nabki},
url = {https://ruc.udc.es/dspace/handle/2183/25870},
year = {2017},
date = {2017-01-01},
journal = {XXXVIII Jornadas de Automática},
pages = {991–998},
abstract = {This paper introduces Oculus-Crawl, a modular and scalable tool designed to automatically collect images from Google and Yahoo Images search engines. The tool enables efficient dataset creation for computer vision tasks, featuring capabilities for storing and sharing large datasets along with their metadata. Testing demonstrated its efficiency by successfully collecting over 11,500 images with their metadata in under 14 minutes.},
note = {Publisher: Servicio de Publicaciones de la Universidad de Oviedo},
keywords = {Computer vision, crawler, Dataset, images, search engine},
pubstate = {published},
tppubtype = {article}
}
This paper introduces Oculus-Crawl, a modular and scalable tool designed to automatically collect images from Google and Yahoo Images search engines. The tool enables efficient dataset creation for computer vision tasks, featuring capabilities for storing and sharing large datasets along with their metadata. Testing demonstrated its efficiency by successfully collecting over 11,500 images with their metadata in under 14 minutes.
2009
2.
González-Castro, V; Alegre, Enrique; Morala-Argüello, P; Suarez, SA
A combined and intelligent new segmentation method for boar semen based on thresholding and watershed transform Artículo de revista
En: International Journal of Imaging, vol. 2, no 9 S, pp. 70–80, 2009, (Publisher: Indian Society for Development and Environment Research).
Resumen | Enlaces | BibTeX | Etiquetas: images, segmentation, segmentation method, semen, threshold, watershed
@article{gonzalez-castro_combined_2009,
title = {A combined and intelligent new segmentation method for boar semen based on thresholding and watershed transform},
author = {V González-Castro and Enrique Alegre and P Morala-Argüello and SA Suarez},
url = {https://www.research.ed.ac.uk/en/publications/a-combined-and-intelligent-new-segmentation-method-for-boar-semen},
year = {2009},
date = {2009-01-01},
journal = {International Journal of Imaging},
volume = {2},
number = {9 S},
pages = {70–80},
abstract = {This work presents a new method to segment images of alive and dead spermatozoa in ositive phase contrast. This method improves previous segmentation methods applying an intelligent threshold combined with watershed segmentation. First, it applies an intelligent thresholding segmentation that changes the value of threshold when the binary image obtained is not fulfill the surface and eccentricity factors. Then, using the same automatic criteria, the bad segmented images are processed by means of the watershed transform. Using this new method a 90.96% of the spermatozoa have been correctly segmented. This approach could be useful to commercial Computer Assisted Semen Analysis systems that need new and more accurate segmentation processes.},
note = {Publisher: Indian Society for Development and Environment Research},
keywords = {images, segmentation, segmentation method, semen, threshold, watershed},
pubstate = {published},
tppubtype = {article}
}
This work presents a new method to segment images of alive and dead spermatozoa in ositive phase contrast. This method improves previous segmentation methods applying an intelligent threshold combined with watershed segmentation. First, it applies an intelligent thresholding segmentation that changes the value of threshold when the binary image obtained is not fulfill the surface and eccentricity factors. Then, using the same automatic criteria, the bad segmented images are processed by means of the watershed transform. Using this new method a 90.96% of the spermatozoa have been correctly segmented. This approach could be useful to commercial Computer Assisted Semen Analysis systems that need new and more accurate segmentation processes.
3.
Castro, Víctor González; Alegre, Enrique; Argüello, P Morala; Suarez, SA
A combined and intelligent new segmentation method for boar semen based on thresholding and watershed transform Artículo de revista
En: International Journal of Imaging, vol. 2, no 9 S, pp. 70–80, 2009, (Publisher: Indian Society for Development and Environment Research).
Resumen | Enlaces | BibTeX | Etiquetas: images, segmentation, segmentation method, semen, threshold, watershed
@article{gonzalez_castro_combined_2009,
title = {A combined and intelligent new segmentation method for boar semen based on thresholding and watershed transform},
author = {Víctor González Castro and Enrique Alegre and P Morala Argüello and SA Suarez},
url = {https://www.research.ed.ac.uk/en/publications/a-combined-and-intelligent-new-segmentation-method-for-boar-semen},
year = {2009},
date = {2009-01-01},
journal = {International Journal of Imaging},
volume = {2},
number = {9 S},
pages = {70–80},
abstract = {This work presents a new method to segment images of alive and dead spermatozoa in ositive phase contrast. This method improves previous segmentation methods applying an intelligent threshold combined with watershed segmentation. First, it applies an intelligent thresholding segmentation that changes the value of threshold when the binary image obtained is not fulfill the surface and eccentricity factors. Then, using the same automatic criteria, the bad segmented images are processed by means of the watershed transform. Using this new method a 90.96% of the spermatozoa have been correctly segmented. This approach could be useful to commercial Computer Assisted Semen Analysis systems that need new and more accurate segmentation processes.},
note = {Publisher: Indian Society for Development and Environment Research},
keywords = {images, segmentation, segmentation method, semen, threshold, watershed},
pubstate = {published},
tppubtype = {article}
}
This work presents a new method to segment images of alive and dead spermatozoa in ositive phase contrast. This method improves previous segmentation methods applying an intelligent threshold combined with watershed segmentation. First, it applies an intelligent thresholding segmentation that changes the value of threshold when the binary image obtained is not fulfill the surface and eccentricity factors. Then, using the same automatic criteria, the bad segmented images are processed by means of the watershed transform. Using this new method a 90.96% of the spermatozoa have been correctly segmented. This approach could be useful to commercial Computer Assisted Semen Analysis systems that need new and more accurate segmentation processes.