Publications
2011
1.
Gutiérrez, El Prof Dr Enrique Alegre
Descripción adaptativa de texturas y estimación de las probabilidades a priori de las clases para el control de calidad seminal Tesis doctoral
Universidad de León, 2011.
Resumen | Enlaces | BibTeX | Etiquetas: Calidad Seminal, Descripción Adaptativa de Texturas, Estimación, Image Texture, Quantification, segmentation, Unlabeled Datasets
@phdthesis{gutierrez_descripcion_2011,
title = {Descripción adaptativa de texturas y estimación de las probabilidades a priori de las clases para el control de calidad seminal},
author = {El Prof Dr Enrique Alegre Gutiérrez},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=en&user=81rvBFwAAAAJ&cstart=20&pagesize=80&sortby=title&citation_for_view=81rvBFwAAAAJ:IjCSPb-OGe4C},
year = {2011},
date = {2011-01-01},
school = {Universidad de León},
abstract = {In this Thesis we have evaluated several approaches to describe digital image textures. In addition, we have proposed a new intelligent segmentation procedure, an original adaptive texture descriptor and two new methods for estimating class proportions (quantification) of unlabelled datasets.},
keywords = {Calidad Seminal, Descripción Adaptativa de Texturas, Estimación, Image Texture, Quantification, segmentation, Unlabeled Datasets},
pubstate = {published},
tppubtype = {phdthesis}
}
In this Thesis we have evaluated several approaches to describe digital image textures. In addition, we have proposed a new intelligent segmentation procedure, an original adaptive texture descriptor and two new methods for estimating class proportions (quantification) of unlabelled datasets.
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.
0000
4.
Martın, Guillermo Martınez San; Robles, Laura Fernández; Alegre, Enrique; Olalla, Oscar Garcıa
A segmentation approach for evaluating wear of inserts in milling machines with computer vision techniques Artículo de revista
En: 0000.
Resumen | Enlaces | BibTeX | Etiquetas: Computer vision, milling machines, segmentation, Tool wear
@article{martinez_san_martin_segmentation_nodate,
title = {A segmentation approach for evaluating wear of inserts in milling machines with computer vision techniques},
author = {Guillermo Martınez San Martın and Laura Fernández Robles and Enrique Alegre and Oscar Garcıa Olalla},
url = {https://scholar.google.es/citations?view_op=view_citation&hl=es&user=4jZgNVkAAAAJ&sortby=title&citation_for_view=4jZgNVkAAAAJ:Se3iqnhoufwC},
abstract = {Measuring tool wear in milling machines is an important task to evaluate the lifetime of the cutting parts (inserts) and deciding whether we should replace them. In our research, we propose to use computer vision algorithms to perform this task. Part of the research is to evaluate the accuracy of different segmentation algorithms that segment the area of wear. We have used two methods: k-Means and Mean Shift. To evaluate the segmentation results the Dice coefficient was used, obtaining with Mean Shift a QS= 0.5923 for all the edges and a QS= 0.6831 just for edges with high wear.},
keywords = {Computer vision, milling machines, segmentation, Tool wear},
pubstate = {published},
tppubtype = {article}
}
Measuring tool wear in milling machines is an important task to evaluate the lifetime of the cutting parts (inserts) and deciding whether we should replace them. In our research, we propose to use computer vision algorithms to perform this task. Part of the research is to evaluate the accuracy of different segmentation algorithms that segment the area of wear. We have used two methods: k-Means and Mean Shift. To evaluate the segmentation results the Dice coefficient was used, obtaining with Mean Shift a QS= 0.5923 for all the edges and a QS= 0.6831 just for edges with high wear.