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Pregled bibliografske jedinice broj: 896853

LeArEst: Length and Area Estimation from Data Measured with Additive Error


Benšić, Mirta; Taler, Petar; Hamedović, Safet; Nyarko, Emmanuel Karlo; Sabo, Kristian
LeArEst: Length and Area Estimation from Data Measured with Additive Error // R Journal, 9 (2017), 2; 461-473 (međunarodna recenzija, članak, znanstveni)


Naslov
LeArEst: Length and Area Estimation from Data Measured with Additive Error

Autori
Benšić, Mirta ; Taler, Petar ; Hamedović, Safet ; Nyarko, Emmanuel Karlo ; Sabo, Kristian

Izvornik
R Journal (2073-4859) 9 (2017), 2; 461-473

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Noisy image, additive error, border estimation, area estimation

Sažetak
This paper describes an R package LeArEst that can be used for estimating object dimensions from a noisy image. The package is based on the 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 if it is elliptically shaped. The input data may be a numerical vector as well as an image in JPEG format. In this paper, background statistical models and methods for the package are summarized, and algorithms and key functions implemented are described. Also, examples that demonstrate its usage are provided.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-IP-2016-06-6545 - Optimizacijski i statistički modeli i metode prepoznavanja svojstava skupova podataka izmjerenih s pogreškama (OSMoMeSIP) (Rudolf Scitovski (42563), )

Ustanove
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek,
Sveučilište u Osijeku, Odjel za matematiku

Časopis indeksira:


  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus