The R Journal: article published in 2017, volume 9:2

LeArEst: Length and Area Estimation from Data Measured with Additive Error
Mirta Benšić, Petar Taler, Safet Hamedović, Emmanuel Karlo Nyarko and Kristian Sabo , The R Journal (2017) 9:2, pages 461-473.

Abstract This paper describes an R package LeArEst that can be used for estimating object dimensions from a noisy image. The package is based on a simple parametric model for data that are drawn from uniform distribution contaminated by an additive error. Our package is able to estimate the length of the object of interest on a given straight line that intersects it, as well as to estimate the object area when it is elliptically shaped. The input data may be a numerical vector or an image in JPEG format. In this paper, background statistical models and methods for the package are summarized, and the algorithms and key functions implemented are described. Also, examples that demonstrate its usage are provided. Availability: LeArEst is available on CRAN.

Received: 2017-07-07; online 2017-10-07, supplementary material, (570 B)
CRAN packages: LeArEst, decon, deamer, conicfit, jpeg, opencpu, shiny
CRAN Task Views implied by cited CRAN packages: WebTechnologies, NumericalMathematics

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-043,
  author = {Mirta Benšić and Petar Taler and Safet Hamedović and
          Emmanuel Karlo Nyarko and Kristian Sabo},
  title = {{LeArEst: Length and Area Estimation from Data Measured with
          Additive Error}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-043},
  url = {https://doi.org/10.32614/RJ-2017-043},
  pages = {461--473},
  volume = {9},
  number = {2}
}