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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2017-043.zip
LeArEst, decon, deamer, conicfit, jpeg, opencpu, shiny
WebTechnologies, NumericalMathematics
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For attribution, please cite this work as
Benšić, et al., "The R Journal: LeArEst: Length and Area Estimation from Data Measured with Additive Error", The R Journal, 2017
BibTeX citation
@article{RJ-2017-043,
author = {Benšić, Mirta and Taler, Petar and Hamedović, Safet and Nyarko, Emmanuel Karlo and Sabo, Kristian},
title = {The R Journal: LeArEst: Length and Area Estimation from Data Measured with Additive Error},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-043},
doi = {10.32614/RJ-2017-043},
volume = {9},
issue = {2},
issn = {2073-4859},
pages = {461-473}
}