Título:	Mathematical models for metric features extraction from RGB-D sensor.
Autor:	SANTOS, E. F. dos VENDRUSCULO, L. G. LOPES, L. B. KAMCHEN, S. G. CONDOTTA, I. C. F. S.
Afiliación:	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.
Año:	2021
Referencia:	Scientific Electronic Archives, v. 14, n. 11, p. 76-85, 2021.
Descripción:	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.
NAL Thesaurus:	Mathematical models Image analysis
Palabras clave:	Modelos matemáticos Processamento de imagem Extração de características Image processing Depth camera RealSenseTM
DOI:	https://doi.org/10.36560/141120211467
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPTIA)