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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Agroenergia. |
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Data corrente: |
01/08/2016 |
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Data da última atualização: |
07/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTOS, T. T.; RODRIGUES, G. C. |
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Afiliação: |
THIAGO TEIXEIRA SANTOS, CNPTIA; GUSTAVO COSTA RODRIGUES, CNPTIA. |
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Título: |
Flexible three-dimensional modeling of plants using low-resolution cameras and visual odometry. |
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Ano de publicação: |
2016 |
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Fonte/Imprenta: |
Machine Vision and Applications, New York, v. 27, n. 5, p. 695-707, 2016. |
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DOI: |
10.1007/s00138-015-0729-3 |
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Idioma: |
Inglês |
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Conteúdo: |
The three-dimensional reconstruction of plants using computer vision methods is a promising alternative to non-destructive metrology in plant phenotyping. However, diversity in plants form and size, different surrounding environments (laboratory, greenhouse or field), and occlusions impose challenging issues. We propose the use of state-ofthe-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereowas employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way. The results show a free-moving low-resolution camera is able to handle occlusions and variations in plant size and form, allowing the reconstruction of different species, and specimens in different stages of development. It is also a cheap and flexible method, suitable for different phenotyping needs. Plant traits were computed from the point clouds and compared to manually measured reference, showing millimeter accuracy. All data, including images, camera calibration, pose, and threedimensional models are publicly available. |
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Palavras-Chave: |
3-D reconstruction; Fenotipagem planta; Image-based phenotyping; Modelos tridimensionais; Odometria visual; Plant digitizing; Reconstrução tridimensional de plantas; Visão computacional; Visual odometry. |
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Thesaurus Nal: |
Computer vision; Image analysis; Models; Phenotype. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02328naa a2200301 a 4500
001 2049954
005 2020-01-07
008 2016 bl uuuu u00u1 u #d
024 7 $a10.1007/s00138-015-0729-3$2DOI
100 1 $aSANTOS, T. T.
245 $aFlexible three-dimensional modeling of plants using low-resolution cameras and visual odometry.$h[electronic resource]
260 $c2016
520 $aThe three-dimensional reconstruction of plants using computer vision methods is a promising alternative to non-destructive metrology in plant phenotyping. However, diversity in plants form and size, different surrounding environments (laboratory, greenhouse or field), and occlusions impose challenging issues. We propose the use of state-ofthe-art methods for visual odometry to accurately recover camera pose and preliminary three-dimensional models on image acquisition time. Specimens of maize and sunflower were imaged using a single free-moving camera and a software tool with visual odometry capabilities. Multiple-view stereowas employed to produce dense point clouds sampling the plant surfaces. The produced three-dimensional models are accurate snapshots of the shoot state and plant measurements can be recovered in a non-invasive way. The results show a free-moving low-resolution camera is able to handle occlusions and variations in plant size and form, allowing the reconstruction of different species, and specimens in different stages of development. It is also a cheap and flexible method, suitable for different phenotyping needs. Plant traits were computed from the point clouds and compared to manually measured reference, showing millimeter accuracy. All data, including images, camera calibration, pose, and threedimensional models are publicly available.
650 $aComputer vision
650 $aImage analysis
650 $aModels
650 $aPhenotype
653 $a3-D reconstruction
653 $aFenotipagem planta
653 $aImage-based phenotyping
653 $aModelos tridimensionais
653 $aOdometria visual
653 $aPlant digitizing
653 $aReconstrução tridimensional de plantas
653 $aVisão computacional
653 $aVisual odometry
700 1 $aRODRIGUES, G. C.
773 $tMachine Vision and Applications, New York$gv. 27, n. 5, p. 695-707, 2016.
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Embrapa Agricultura Digital (CNPTIA) |
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| 19. |  | SANTOS, T. T.; KOENIGKAN, L. V.; BARBEDO, J. G. A.; RODRIGUES, G. C. 3D plant modeling: localization, mapping and segmentation for plant phenotyping using a single hand-held camera. In: EUROPEAN CONFERENCE ON COMPUTER VISION, 13., 2014, Zurich. Computer Vision: ECCV 2014: proceedings. Switzerland: Springer, 2015. p. 247-263. (Lecture notes in computer science, v. 8928). Part IV. Editores: Lourdes Agapito, Michael M. Bronstein, Carsten Rother.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 208 | |
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