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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
19/05/2015 |
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Data da última atualização: |
22/02/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTOS, T. T. |
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Afiliação: |
THIAGO TEIXEIRA SANTOS, CNPTIA. |
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Título: |
SciPy and OpenCV as an interactive computing environment for computer vision. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Revista de Informática Teórica e Aplicada, v. 22, n. 1, p. 154-189, 2015. |
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Idioma: |
Inglês |
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Conteúdo: |
In research and development (R&D), interactive computing environments are a frequently employed alternative for data exploration, algorithm development and prototyping. In the last twelve years, a popular scientific computing environment flourished around the Python programming language. Most of this environment is part of (or built over) a software stack named SciPy Stack. Combined with OpenCV?s Python interface, this environment becomes an alternative for current computer vision R&D. This tutorial introduces such an environment and shows how it can address different steps of computer vision research, from initial data exploration to parallel computing implementations. Several code examples are presented. They deal with problems from simple image processing to inference by machine learning. All examples are also available as IPython notebooks. |
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Palavras-Chave: |
Computação. |
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Thesaurus Nal: |
Computer vision; Digital images; Image analysis. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/124156/1/SciPy.PDF
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Marc: |
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001 2015739
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008 2015 bl uuuu u00u1 u #d
100 1 $aSANTOS, T. T.
245 $aSciPy and OpenCV as an interactive computing environment for computer vision.$h[electronic resource]
260 $c2015
520 $aIn research and development (R&D), interactive computing environments are a frequently employed alternative for data exploration, algorithm development and prototyping. In the last twelve years, a popular scientific computing environment flourished around the Python programming language. Most of this environment is part of (or built over) a software stack named SciPy Stack. Combined with OpenCV?s Python interface, this environment becomes an alternative for current computer vision R&D. This tutorial introduces such an environment and shows how it can address different steps of computer vision research, from initial data exploration to parallel computing implementations. Several code examples are presented. They deal with problems from simple image processing to inference by machine learning. All examples are also available as IPython notebooks.
650 $aComputer vision
650 $aDigital images
650 $aImage analysis
653 $aComputação
773 $tRevista de Informática Teórica e Aplicada$gv. 22, n. 1, p. 154-189, 2015.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 110 | |
| 3. |  | 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 |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| 14. | | SANTOS, A. A. dos; SANTOS, T. T. Estudo de métodos de aprendizagem profunda para reconhecimento de bagas de uva. In: MOSTRA DE ESTAGIÁRIOS E BOLSISTAS DA EMBRAPA INFORMÁTICA AGROPCUÁRIA, 13., 2017, Campinas. Resumos expandidos... Brasília, DF: Embrapa, 2017. p. 43-46. Editores técnicos: Giampaolo Queiroz Pellegrino, Luciana Guilherme Sacomani Zenerato, Maria Fernanda Moura, Giulia Croce, Poliana Fernanda Giachetto.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 110 | |
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