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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Agrossilvipastoril. |
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Data corrente: |
04/01/2022 |
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Data da última atualização: |
06/01/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SANTOS, E. F. dos; VENDRUSCULO, L. G.; LOPES, L. B.; KAMCHEN, S. G.; CONDOTTA, I. C. F. S. |
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Afiliação: |
ELTON FERNANDES DOS SANTOS, UFMT; LAURIMAR GONCALVES VENDRUSCULO, CNPTIA; LUCIANO BASTOS LOPES, CPAMT; SCHEILA GEIELE KAMCHEN, UFMT; ISABELLA C. F. S. CONDOTTA, University of Illinois. |
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Título: |
Mathematical models for metric features extraction from RGB-D sensor. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Scientific Electronic Archives, v. 14, n. 11, p. 76-85, 2021. |
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DOI: |
https://doi.org/10.36560/141120211467 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract. The use of the RGB-D camera has been applied in several fields of science. That popularization is due to the emergence of technologies such as the Intel® RealSenseTM D400 series. However, despite the actual demand from some potential users, few studies concern the characterization of these sensors for object measurements. Our study sought to estimate models dealing with calculating the area and length between targets or points within RGB and depth images. An experiment was set up with white cardboard fixed on a flat surface with colored pins. We measured the distance between the camera and cardboard by calculating the average distance from the pixels belonging to the target area. The Information Criterion AIC and BIC associated with R2 were performed to select the best models. Polynomial and power regression models reached the highest coefficient of determination and smallest values of AIC and BIC. |
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Palavras-Chave: |
Depth camera; Extração de características; Image processing; Modelos matemáticos; Processamento de imagem; RealSenseTM. |
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Thesaurus Nal: |
Image analysis; Mathematical models. |
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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/229943/1/AP-Mathematical-models-2021.pdf
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Marc: |
LEADER 01776naa a2200277 a 4500
001 2138727
005 2022-01-06
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.36560/141120211467$2DOI
100 1 $aSANTOS, E. F. dos
245 $aMathematical models for metric features extraction from RGB-D sensor.$h[electronic resource]
260 $c2021
520 $aAbstract. The use of the RGB-D camera has been applied in several fields of science. That popularization is due to the emergence of technologies such as the Intel® RealSenseTM D400 series. However, despite the actual demand from some potential users, few studies concern the characterization of these sensors for object measurements. Our study sought to estimate models dealing with calculating the area and length between targets or points within RGB and depth images. An experiment was set up with white cardboard fixed on a flat surface with colored pins. We measured the distance between the camera and cardboard by calculating the average distance from the pixels belonging to the target area. The Information Criterion AIC and BIC associated with R2 were performed to select the best models. Polynomial and power regression models reached the highest coefficient of determination and smallest values of AIC and BIC.
650 $aImage analysis
650 $aMathematical models
653 $aDepth camera
653 $aExtração de características
653 $aImage processing
653 $aModelos matemáticos
653 $aProcessamento de imagem
653 $aRealSenseTM
700 1 $aVENDRUSCULO, L. G.
700 1 $aLOPES, L. B.
700 1 $aKAMCHEN, S. G.
700 1 $aCONDOTTA, I. C. F. S.
773 $tScientific Electronic Archives$gv. 14, n. 11, p. 76-85, 2021.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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