Mathematical models for metric features extraction from RGB-D sensor
| Autor: | Scheila Geiele Kamchen, Elton Fernandes dos Santos, Laurimar Gonçalves Vendrusculo, Luciano Bastos Lopes, Isabella C. F. S. Condotta |
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| Přispěvatelé: | 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. |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
RealSenseTM
Polynomial Mathematical models Pixel Mathematical model business.industry Image processing Pattern recognition General Works Image analysis Set (abstract data type) Modelos matemáticos Colored Processamento de imagem Metric (mathematics) Depth camera RGB color model Artificial intelligence image processing depth camera realsense™ business Extração de características Mathematics |
| Zdroj: | Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice) Empresa Brasileira de Pesquisa Agropecuária (Embrapa) instacron:EMBRAPA Scientific Electronic Archives, Vol 14, Iss 11 (2021) |
| Popis: | 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. Made available in DSpace on 2022-01-04T18:00:42Z (GMT). No. of bitstreams: 1 AP-Mathematical-models-2021.pdf: 456104 bytes, checksum: 504e87c69b1133646b857a3ea03c0199 (MD5) Previous issue date: 2021 |
| Databáze: | OpenAIRE |
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