Publicação
SciPy and OpenCV as an interactive computing environment for computer vision.
| Resumo: | 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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| Autores principais: | SANTOS, T. T. |
| Assunto: | Computação Computer vision Digital images Image analysis |
| Ano: | 2015 |
| País: | Brasil |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | IBICT |
| Idioma: | inglês |
| Origem: | Oasisbr |
| Resumo: | 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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