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Registro completo
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Biblioteca (s) : |
INIA Treinta y Tres. |
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Fecha : |
03/03/2020 |
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Actualizado : |
03/03/2020 |
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Tipo de producción científica : |
Abstracts/Resúmenes |
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Autor : |
SCHEFFEL, S.; PÉREZ DE VIDA, F. |
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Afiliación : |
SHEILA SCHEFFEL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; FERNANDO BLAS PEREZ DE VIDA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Rice ecophysiology: climatic variables and yield at Uruguay. [Extended abstract] + [Poster]. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
In: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020. |
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ISBN : |
978-65-00-00331-4 |
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Idioma : |
Inglés |
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Palabras claves : |
ELITE GERMPLASM; INDICA TYPE; TROPICAL JAPONICA. |
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Thesagro : |
ARROZ; ECOFISIOLOGIA DEL CULTIVO; GERMOPLASMA. |
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Asunto categoría : |
F30 Genética vegetal y fitomejoramiento |
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URL : |
http://www.ainfo.inia.uy/digital/bitstream/item/14257/1/IRTC-2020-Scheffel-3-Abstract.pdf
http://www.ainfo.inia.uy/digital/bitstream/item/14258/1/IRTC-2020-Scheffel-4-Poster.pdf
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Marc : |
LEADER 00789naa a2200205 a 4500
001 1060866
005 2020-03-03
008 2020 bl uuuu u00u1 u #d
020 $a978-65-00-00331-4
100 1 $aSCHEFFEL, S.
245 $aRice ecophysiology$bclimatic variables and yield at Uruguay. [Extended abstract] + [Poster].$h[electronic resource]
260 $c2020
650 $aARROZ
650 $aECOFISIOLOGIA DEL CULTIVO
650 $aGERMOPLASMA
653 $aELITE GERMPLASM
653 $aINDICA TYPE
653 $aTROPICAL JAPONICA
700 1 $aPÉREZ DE VIDA, F.
773 $tIn: International Temperate Rice Conference (7., 2020, Pelotas, RS), Science & Innovation: feeding a world of 10 billion people: proceedings. Pelotas RS, Brasil, February 9-12, 2020. Brasília, DF : Embrapa, 2020.
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Registro original : |
INIA Treinta y Tres (TT) |
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 | Acceso al texto completo restringido a Biblioteca INIA Tacuarembó. Por información adicional contacte bibliotb@tb.inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Tacuarembó. |
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Fecha actual : |
14/03/2017 |
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Actualizado : |
05/06/2018 |
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Tipo de producción científica : |
Capítulo en Libro Técnico-Científico |
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Autor : |
GÓMEZ, A.; CARBAJAL, G.; FUENTES, M.; VIÑOLES, C. |
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Afiliación : |
ÁLVARO GÓMEZ; GUILLERMO CARBAJAL; MAGDALENA FUENTES; CAROLINA VIÑOLES GIL, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay. |
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Título : |
Detection of follicles in ultrasound videos of bovine ovaries. |
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Fecha de publicación : |
2017 |
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Fuente / Imprenta : |
In: Beltrán-Castañón C.; Nyström I.; Famili F. (eds.). Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. Lecture Notes in Computer Science, vol 10125. Springer, Cham, 2017. |
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Páginas : |
p. 352-359 |
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DOI : |
10.1007/978-3-319-52277-7_43 |
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Idioma : |
Inglés |
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Contenido : |
Ultrasound imaging is a veterinarian standard procedure for the monitoring of ovarian structures in cattle. Recent studies, suggest that the number of antral follicles can give a cue of the future fertility of a specimen. Therefore, there has been a growing interest in counting the number of antral follicles at early stages in life.
In the most typical procedure, the operator performs a trans-rectal ultrasound scan and counts the follicles on the live video that is seen in the ultrasound machine. This is a challenging task and requires highly trained experts that can reliably detect and count the follicles in a quick sweep of a few seconds.
This work presents the integration of several signal processing techniques to the problem of automatically detecting follicles in ultrasound videos of bovine cattle ovaries. The approach starts from an ultrasound video that traverses the ovary from end to end. Putative follicle regions are detected on each frame with a cascade of boosted classifiers. In order to impose temporal coherence, the detections are tracked across the frames with multiple Kalman filters. The tracks are analyzed to separate follicle detections from other false detections.
The method is tested on a phantom dataset of ovaries in gelatin with dissection ground truth. Results are promising and encourage further extension to in-vivo ultrasound videos.
© Springer International Publishing AG 2017. |
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Palabras claves : |
CASCADE CLASSIFIER; FOLLICLE DETECTION; MULTITRACKING. |
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Thesagro : |
ECOGRAFIA; ULTRASONIDO. |
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Asunto categoría : |
L53 Fisiología Animal - Reproducción |
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Marc : |
LEADER 02272naa a2200241 a 4500
001 1056832
005 2018-06-05
008 2017 bl uuuu u00u1 u #d
024 7 $a10.1007/978-3-319-52277-7_43$2DOI
100 1 $aGÓMEZ, A.
245 $aDetection of follicles in ultrasound videos of bovine ovaries.$h[electronic resource]
260 $c2017
300 $ap. 352-359
520 $aUltrasound imaging is a veterinarian standard procedure for the monitoring of ovarian structures in cattle. Recent studies, suggest that the number of antral follicles can give a cue of the future fertility of a specimen. Therefore, there has been a growing interest in counting the number of antral follicles at early stages in life. In the most typical procedure, the operator performs a trans-rectal ultrasound scan and counts the follicles on the live video that is seen in the ultrasound machine. This is a challenging task and requires highly trained experts that can reliably detect and count the follicles in a quick sweep of a few seconds. This work presents the integration of several signal processing techniques to the problem of automatically detecting follicles in ultrasound videos of bovine cattle ovaries. The approach starts from an ultrasound video that traverses the ovary from end to end. Putative follicle regions are detected on each frame with a cascade of boosted classifiers. In order to impose temporal coherence, the detections are tracked across the frames with multiple Kalman filters. The tracks are analyzed to separate follicle detections from other false detections. The method is tested on a phantom dataset of ovaries in gelatin with dissection ground truth. Results are promising and encourage further extension to in-vivo ultrasound videos. © Springer International Publishing AG 2017.
650 $aECOGRAFIA
650 $aULTRASONIDO
653 $aCASCADE CLASSIFIER
653 $aFOLLICLE DETECTION
653 $aMULTITRACKING
700 1 $aCARBAJAL, G.
700 1 $aFUENTES, M.
700 1 $aVIÑOLES, C.
773 $tIn: Beltrán-Castañón C.; Nyström I.; Famili F. (eds.). Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2016. Lecture Notes in Computer Science, vol 10125. Springer, Cham, 2017.
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