Open AccessMathematical models for metric features extraction from RGB-D sensor
Author(s) -
Elton Fernandes dos Santos,
Laurimar Gonçalves Vendrusculo,
Luciano Bastos Lopes,
Scheila Geiele Kamchen,
Isabella C. F. S. Condotta
Publication year - 2021
Publication title -
scientific electronic archives
Language(s) - English
Resource type - Journals
ISSN - 2316-9281
DOI - 10.36560/141120211467
Subject(s) - rgb color model , artificial intelligence , metric (unit) , cardboard , pixel , polynomial regression , computer science , set (abstract data type) , computer vision , mathematics , colored , pattern recognition (psychology) , regression analysis , statistics , engineering , mechanical engineering , operations management , programming language , materials science , composite material
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® RealSense™ 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.