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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. |
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Data corrente: |
10/04/2015 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
SANTOS, T. T.; KOENIGKAN, L. V.; BARBEDO, J. G. A.; RODRIGUES, G. C. |
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Afiliação: |
THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO VIEIRA KOENIGKAN, CNPTIA; JAYME GARCIA ARNAL BARBEDO, CNPTIA; GUSTAVO COSTA RODRIGUES, CNPTIA. |
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Título: |
3D plant modeling: localization, mapping and segmentation for plant phenotyping using a single hand-held camera. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
In: EUROPEAN CONFERENCE ON COMPUTER VISION, 13., 2014, Zurich. Computer Vision: ECCV 2014: proceedings. Switzerland: Springer, 2015. |
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Páginas: |
p. 247-263. |
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Série: |
(Lecture notes in computer science, v. 8928). |
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DOI: |
10.1007/978-3-319-16220-1_18 |
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Idioma: |
Inglês |
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Notas: |
Part IV. Editores: Lourdes Agapito, Michael M. Bronstein, Carsten Rother. |
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Conteúdo: |
Abstract. Functional-structural modeling and high-throughput phenomics demand tools for 3D measurements of plants. In this work, structure from motion is employed to estimate the position of a hand-held camera, moving around plants, and to recover a sparse 3D point cloud sampling the plants? surfaces. Multiple-view stereo is employed to extend the sparse model to a dense 3D point cloud. The model is automatically segmented by spectral clustering, properly separating the plant?s leaves whose surfaces are estimated by fitting trimmed B-splines to their 3D points. These models are accurate snapshots for the aerial part of the plants at the image acquisition moment and allow the measurement of different features of the specimen phenotype. Such state-of-the-art computer vision techniques are able to produce accurate 3D models for plants using data from a single free moving camera, properly handling occlusions and diversity in size and structure for specimens presenting sparse canopies. A data set formed by the input images and the resulting camera poses and 3D points clouds is available, including data for sunflower and soybean specimens. |
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Palavras-Chave: |
Fenotipagem de planta; Modelagem de planta; Plant phenotyping; Segmentação; Segmentation; SLAM; Structure from motion; Visão computacional. |
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Thesaurus Nal: |
Computer vision. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02245nam a2200301 a 4500
001 2013267
005 2020-01-08
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1007/978-3-319-16220-1_18$2DOI
100 1 $aSANTOS, T. T.
245 $a3D plant modeling$blocalization, mapping and segmentation for plant phenotyping using a single hand-held camera.$h[electronic resource]
260 $aIn: EUROPEAN CONFERENCE ON COMPUTER VISION, 13., 2014, Zurich. Computer Vision: ECCV 2014: proceedings. Switzerland: Springer$c2015
300 $ap. 247-263.
490 $a(Lecture notes in computer science, v. 8928).
500 $aPart IV. Editores: Lourdes Agapito, Michael M. Bronstein, Carsten Rother.
520 $aAbstract. Functional-structural modeling and high-throughput phenomics demand tools for 3D measurements of plants. In this work, structure from motion is employed to estimate the position of a hand-held camera, moving around plants, and to recover a sparse 3D point cloud sampling the plants? surfaces. Multiple-view stereo is employed to extend the sparse model to a dense 3D point cloud. The model is automatically segmented by spectral clustering, properly separating the plant?s leaves whose surfaces are estimated by fitting trimmed B-splines to their 3D points. These models are accurate snapshots for the aerial part of the plants at the image acquisition moment and allow the measurement of different features of the specimen phenotype. Such state-of-the-art computer vision techniques are able to produce accurate 3D models for plants using data from a single free moving camera, properly handling occlusions and diversity in size and structure for specimens presenting sparse canopies. A data set formed by the input images and the resulting camera poses and 3D points clouds is available, including data for sunflower and soybean specimens.
650 $aComputer vision
653 $aFenotipagem de planta
653 $aModelagem de planta
653 $aPlant phenotyping
653 $aSegmentação
653 $aSegmentation
653 $aSLAM
653 $aStructure from motion
653 $aVisão computacional
700 1 $aKOENIGKAN, L. V.
700 1 $aBARBEDO, J. G. A.
700 1 $aRODRIGUES, G. C.
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| 11. |  | SANTOS, T. T. Detecção automática de bagas de café em imagens de campo. In: CONGRESSO BRASILEIRO DE AGROINFORMÁTICA, 10., 2015, Ponta Grossa. Uso de VANTs e sensores para avanços no agronegócio: anais. Ponta Grossa: Universidade Estadual de Ponta Grossa, 2015. Não paginado. SBIAgro 2015.| Tipo: Artigo em Anais de Congresso |
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